Riding down, riding down
My hand on your seat
The whole way round
I carry band-aids on me now
For when your soft hands hit the jagged ground
Wheels arent even touching the ground
Scared to take them off but theyre so worn down
Promise I wont push you straight to the dirt
If you promise me youll take them off first
I love everything you do
When you call me fucking dumb for the stupid shit I do
I wanna ride my bike with you
Fully undressed, no training wheels left for you
Ill pull em off for you
I love everything you do
When you call me fucking dumb for the stupid shit I do
I wanna ride my bike with you
Fully undressed, no training wheels left for you
Ill pull em all off for you
Letting go, letting go
Telling you things you already know
I explode, I explode
Asking you where you want us to go
Youve been riding two wheelers all your life
Its not like Im asking to be your wife
I wanna make you mine, but thats hard to say
Is this coming off in a cheesy way?
I love everything you do
When you call me fucking dumb for the stupid shit I do
I wanna ride my bike with you
Fully undressed, no training wheels left for you
Ill pull em all off for you
I love everything you do
When you call me fucking dumb for the stupid shit I do
I wanna ride my bike with you
Fully undressed, no training wheels left for you
Ill pull em all off for you

I love everything you do
When you call me fucking dumb for the stupid shit I do
I wanna ride my bike with you
Fully undressed, no training wheels left for you
Ill pull em all off for you
I love everything you do
When you call me fucking dumb for the stupid shit I do
I wanna ride my bike with you
Fully undressed, no training wheels left for you
Ill pull em all off for you


The faster were falling, were stopping and stalling
Were running in circles again
Just as things were looking up, you said it wasnt good enough
But still, were trying one more time
Maybe were just trying too hard
When really, its closer than it is too far
Cause Im in too deep, and Im trying to keep
Up above in my head, instead of going under
Cause Im in too deep, and Im trying to keep
Up above in my head, instead of going under
Instead of going under
Seems like each time Im with you, I lose my mind
Because Im bending over backwards to relate
Its one thing to complain, but when youre driving me insane
Well then, I think its time that we took a break
Maybe were just trying too hard
When really, its closer than it is too far
Cause Im in too deep, and Im trying to keep
Up above in my head, instead of going under
Cause Im in too deep, and Im trying to keep
Up above in my head, instead of going under
Instead of going under
Instead of going under
I cant sit back and wonder why
It took so long for this to die
And I hate it when you fake it
You cant hide it, you might as well embrace it
So believe me, its not easy
It seems that somethings telling me
Im in too deep, and Im trying to keep
Up above in my head, instead of going under
Cause Im in too deep, and Im trying to keep
Up above in my head, instead of going under
Instead of going under
Instead of going under again
Instead of going under
Instead of going under again
Instead of going under again
Instead of going under again


I tried to be perfect, but nothing was worth it
I dont believe it makes me real
I thought itd be easy, but no one believes me
I meant all the things I said
If you believe its in my soul
Id say all the words that I know
Just to see if it would show
That Im trying to let you know
That Im better off on my own
This place is so empty, my thoughts are so tempting
I dont know how it got so bad
Sometimes its so crazy that nothing can save me
But its the only thing that I have
If you believe its in my soul
Id say all the words that I know
Just to see if it would show
That Im trying to let you know
That Im better off on my own
On my own!
I tried to be perfect, it just wasnt worth it
Nothing could ever be so wrong
Its hard to believe me, it never gets easy
I guess I knew that all along
If you believe its in my soul
Id say all the words that I know
Just to see if it would show
That Im trying to let you know
That Im better off on my own


So am I still waiting
For this world to stop hating?
Cant find a good reason
Cant find hope to believe in
Drop dead
A bullet to my head
Your words are like a gun in hand
You cant change the state of the nation
We just need some motivation
These eyes
Have seen no conviction
Just lies and more contradiction
So tell me what would you say
Id say its up to me
So am I still waiting
For this world to stop hating?
Cant find a good reason
Cant find hope to believe in
Ignorance
And understanding
Were the first ones to jump in line
Out of step for what we believe in
But whos left to stop the bleeding?
How far
Will we take this?
Its not hard to see through the fakeness
So tell me what would you say
Id say its up to me
So am I still waiting
For this world to stop hating?
Cant find a good reason
Cant find hope to believe
This cant last forever
Time wont make things better
I feel so alone
Cant help myself
And no one knows
If this
Is worthless
Tell me
So
What have we done
With a war that cant be won?
This cant be real
Cause I dont know what to feel, no
So am I still waiting
For this world to stop hating?
Cant find a good reason
Cant find hope to believe
So am I still waiting
For this world to stop hating?
Cant find a good reason
For this world to believe


I dont want this moment to ever end
Where everythings nothing without you
Id wait here forever just to, to see you smile
Cause its true, I am nothing without you
Through it all, Ive made my mistakes
I stumble and fall, but I mean these words
I want you to know, with everything I wont let this go
These words are my heart and soul
Ill hold on to this moment, you know
As I bleed my heart out to show, and I wont let go
Thoughts ran unspoken, forever in vow
And pieces of memories fall to the ground
I know what I didnt have, so I wont let this go
Cause its true, I am nothing without you
All the streets where I walked alone
With nowhere to go have come to an end
I want you to know, with everything I wont let this go
These words are my heart and soul
Ill hold on to this moment, you know
As I bleed my heart out to show, and I wont let go
In front of your eyes, it falls from the skies
When you dont know what youre looking to find
In front of your eyes, it falls from the skies
When you just never know what you will find
What you will find, what you will find
What you will find, what you will find
I dont want this moment to ever end
Where everythings nothing without you
I want you to know, with everything I wont let this go
These words are my heart and soul
Ill hold on to this moment, you know
As I bleed my heart out to show, and I wont let go
I want you to know, with everything I wont let this go
These words are my heart and soul
Ill hold on to this moment, you know
As I bleed my heart out to show, and I wont let go


Everybodys got their problems
Everybody says the same things to you
Its just a matter how you solve them
And knowing how to change the things youve been through
I feel Ive come to realize
How fast life can be compromised
Step back to see whats going on
I cant believe this happened to you
Its just a problem that Im faced with; am I
Not the only one who hates to stand by?
Complications that are first in this line
With all these pictures running through my mind
Knowing endless consequences
I feel so useless in this
Get back, step back, and as for me
I cant believe
Part of me, wont agree
Cause I dont know if its for sure
Suddenly, suddenly
I dont feel so insecure
Part of me, wont agree
Cause I dont know if its for sure
Suddenly, suddenly
I dont feel so insecure anymore
Everybodys got their problems
Everybody says the same things to you
Its just a matter how you solve them
But what else are we supposed to do?
Part of me, wont agree
Cause I dont know if its for sure
Suddenly, suddenly
I dont feel so insecure
Part of me, wont agree
Cause I dont know if its for sure
Suddenly, suddenly
I dont feel so insecure anymore
Why do things that matter the most
Never end up being what we chose
Now that I find no way so bad
I dont think I knew what I had
Why do things that matter the most
Never end up being what we chose
Now that I find no way so bad
I dont think I knew what I had


I havent been home for a while, Im sure everythings the same
Mom and dad both in denial, an only child to take the blame
Sorry mom, but I dont miss you, fathers no name you deserve
Im just a kid with no ambitions, wouldnt come home for the world
Youll never know what Ive become, the king of all thats said and
Done, the forgotten son
This citys buried in defeat, I walk along these no name streets
Wave goodbye to all as I fall...
At the dead end I begin to burn the bridge of innocence
Satisfaction guaranteed a pill away catastrophe
On a mission, nowhere bound, inhibitions underground
A shallow grave I have dug all by myself
And now Ive been gone for so long
I cant remember who was wrong
All innocence is long gone
I pledge allegiance to a world of disbelief where I belong
A walking disaster, the son of all bastards
You regret you made me, its too late to save me
And he said, Far as I can tell, its just voices in my head
Am I talking to myself cause I dont know what I just said?
And she said, Far is where I fell, maybe Im better off dead
Am I at the end of nowhere, is this as good as it gets?
And now Ive been gone for so long
I cant remember who was wrong
All innocence is long gone
I pledge allegiance to a world of disbelief where I belong
A walking disaster, the son of all bastards
You regret you made me, its too late to save me
To save me, to save me, to save me, to save me
To save me, to save me, to save me, to save me
To save me, to save me, to save me, to save me
To save me, to save me, to save me, to save me
To save me, to save me, to save me, to save me
To save me, to save me, to save me, to save me
To save me, to save me, to save me, to save me
To save me, to save me, to save me, to save me
To save me, to save me, to save me, to save me
To save me, to save me, to save me...
And now Ive been gone for so long
I cant remember who was wrong
All innocence is long gone
I pledge allegiance to a world of disbelief where I belong
A walking disaster, the son of all bastards
You regret you made me, its too late to save me
I will be home in a while, you dont have to say a word
I cant wait to see you smile, wouldnt miss it for the world
I will be home in a while, you dont have to say a word
I cant wait to see you smile, wouldnt miss it for the world


Zero chances, no way out
No solutions, give in now
Theres no point resisting, theres nobody listening
Are we just sick?
Are we just tired?
Apathetic, uninspired
Whats to believe in, theres nobody leading
So tell me whats real
I dont know if I can feel
All that we have is just slipping away
And I dont believe that its gonna be okay
You cant stop the bleeding, its almost too late
Youre leaving us all behind with hell to pay
All of the dearly beloved
Your time is up, theyre out for blood
Misery made us so hopelessly faithless
And now I cant believe a single word that you say
Secrets kept, a thin disguise, empty threats are full of lies
Do what you will, you just add to your bill now
So tell me whats right
I dont see the light
All that we have is just slipping away
And I dont believe that its gonna be okay
You cant stop the bleeding, its almost too late
Youre leaving us all behind with hell to pay
Silence, they dont hear you calling
The further down you fall
No, theyre not there at all
Cause theyre already gone
So tell me whats real
I dont know if I can feel
So tell me whats right
I dont see the light
All that we have is just slipping away
And I dont believe that its gonna be okay
You cant stop the bleeding, its almost too late
Youre leaving us all behind with hell to pay


Slow me down two seconds
I dont want to run
Wont you wait for a minute
Im smoother when Im buzzed
And I wont let the fear that I could be something
Play me like Im someone whos better looking
Im losing the game to all the younger kids
But I wont let them fuck with me or what I want
I played it off like Im a winner
I-I-Im living inside of an apartment that is darkest at noon
Im turning bottles into oceans
I-I-IIl tell you whatever I want and let you think its the truth
Cool me down for a minute
So I can start to treat you better
Hold my thumb when I need it
Like a man who gets what he wants
And I wont let the heat that we are missing
Play me like Im someone whos never been here before
I played it off like Im a winner
I-I-Im living inside of an apartment that is darkest at noon
Im turning bottles into oceans
I-I-IIl tell you whatever I want and let you think its the truth
Sometimes its easy to get what you want
Sometimes its easy to lose when you thought you would win
Sometimes you feelings feel like a front
But I wont play the game if I know that the score dont lead to you
I played it off like Im a winner
I-I-Im living inside of an apartment that is darkest at noon
Im turning bottles into oceans
I-I-IIl tell you whatever I want and let you think its the truth
I played it off like Im a winner
I-I-Im living inside of an apartment that is darkest at noon
Im turning bottles into oceans
I-I-IIl tell you whatever I want and let you think its the truth


Even though its cold outside
The fog aint pushin through my mind
I think Ive seen enough of you
But have I had enough of you?
I think its gonna rain tonight
Cover up the stains with white
Cause heavens always blue
An unattractive truth
The truth
You know that Ive been broken down before
Now its just something else I cant explore
I feel like every time it takes less and less
To move on with my mind, theres nothing left
Now I want all of those days, I want the sun in my eyes
I want more of those feelings crawling up my spine
Every day I want more than I
I want more of you
Ive been waiting for you to call my bluff
I can feel the difference, steaming smoke glides in my mind
These bones are dry
You know that Im trying to get you to rise
Steel between my veins, I cant describe but this fires nice
These shadows lie
You know that Ive been broken down before
Now its just something else I cant explore
I feel like every time it takes less and less
To move on with my mind, theres nothing left
Now I want all of those days, I want the sun in my eyes
I want more of those feelings crawling up my spine
Every day I want more than I
I want more of you
All of those days, I want the sun in my eyes
I want more of those feelings crawling up my spine
Every day I want more than I
I want more of you


I remember when we were young
When the smoke would cover the sun
And we would both lose our jobs
At least for a week
The ocean looked like one giant grave
You know Ive always seen it that way
But now I feel its our time to see if I can swim
She said go
Wherever you like
You got time
You got time
To pick the places that youll fall
To pick the people that you want
But she said
No, the bottles dry
And that wont save you
That wont save you
But you got time
Yeah, you got time
Cause you got time
Yeah, you got time
Before we bur-ur-ur-ur-urn
Bur-ur-ur-ur-urn
Bur-ur-ur-ur-urn
Before we bur-ur-ur-ur-urn
Bur-ur-ur-ur-urn
Bur-ur-ur-ur-urn
I remember talking the same
Getting over every day
Watching everyone else drink
Cause I cant stand the taste
The sky was getting darker each day
The hours backed for miles each way
And I felt that it was her time to learn to swim
She said go
Wherever you like
You got time
You got time
To pick the places that youll fall
To pick the people that you want
But she said
No, the bottles dry
And that wont save you
That wont save you
But you got time
Yeah, you got time
Cause you got time
Yeah, you got time
Before we bur-ur-ur-ur-urn
Bur-ur-ur-ur-urn
Bur-ur-ur-ur-urn
Before we bur-ur-ur-ur-urn
Bur-ur-ur-ur-urn
Bur-ur-ur-ur-urn
She said go
Wherever you like
That wont save you
That wont save you
But you got time
You got time
Cause you got time
Yeah you got time
She said no
The bottles dry
And that wont save you
That wont save you
But you got time
Yeah you got time
Cause you got time
Yeah you got time


I laid on my back
An echo of silence
Floating on top a shallow pool of self-awareness
Or a lack thereof
When I saw your face and I knew
That you were drunk
You were drunk
I laid on my back
An echo of silence
Floating on top a shallow pool of self-awareness
Or a lack thereof
When I saw your face and I knew
That you had just come
And you were far away from done
All we want to do is to be something
To be something were not
But all we really want is to have someone
To hold us when were drunk
When were drunk
Its more than just sex
Its more than our bodies
Its more than a movement made from self-loathing and irony
But I dont know what to call it just quite yet
So Im gonna shut up
Cause Im so far away from done
All we want to do is to be something
Be something were not
But all we really want is to have someone
To hold us when were drunk
When were drunk
All we want to do is to be something
Be something were not
And all we really want is to have someone
To hold us when were drunk
I was drunk


---
language: en
tags: deberta-v1
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---

## DeBERTa: Decoding-enhanced BERT with Disentangled Attention

[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on  majority of NLU tasks with 80GB training data.

Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.

#### Fine-tuning on NLU tasks

We present the dev results on SQuAD 1.1/2.0 and MNLI tasks.

| Model             | SQuAD 1.1 | SQuAD 2.0 | MNLI-m |
|-------------------|-----------|-----------|--------|
| RoBERTa-base      | 91.5/84.6 | 83.7/80.5 | 87.6   |
| XLNet-Large       | -/-       | -/80.2    | 86.8   |
| **DeBERTa-base**  | 93.1/87.2 | 86.2/83.1 | 88.8   |

### Citation

If you find DeBERTa useful for your work, please cite the following paper:

``` latex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```


---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---

# BERT base model (uncased)

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
between english and English.

Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
the Hugging Face team.

## Model description

BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:

- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
  the entire masked sentence through the model and has to predict the masked words. This is different from traditional
  recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
  GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
  sentence.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
  predict if the two sentences were following each other or not.

This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.

## Intended uses & limitations

You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
fine-tuned versions on a task that interests you.

Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.

### How to use

You can use this model directly with a pipeline for masked language modeling:

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")

[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
  'score': 0.1073106899857521,
  'token': 4827,
  'token_str': 'fashion'},
 {'sequence': "[CLS] hello i'm a role model. [SEP]",
  'score': 0.08774490654468536,
  'token': 2535,
  'token_str': 'role'},
 {'sequence': "[CLS] hello i'm a new model. [SEP]",
  'score': 0.05338378623127937,
  'token': 2047,
  'token_str': 'new'},
 {'sequence': "[CLS] hello i'm a super model. [SEP]",
  'score': 0.04667217284440994,
  'token': 3565,
  'token_str': 'super'},
 {'sequence': "[CLS] hello i'm a fine model. [SEP]",
  'score': 0.027095865458250046,
  'token': 2986,
  'token_str': 'fine'}]
```

Here is how to use this model to get the features of a given text in PyTorch:

```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

and in TensorFlow:

```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```

### Limitations and bias

Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions:

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("The man worked as a [MASK].")

[{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
  'score': 0.09747550636529922,
  'token': 10533,
  'token_str': 'carpenter'},
 {'sequence': '[CLS] the man worked as a waiter. [SEP]',
  'score': 0.0523831807076931,
  'token': 15610,
  'token_str': 'waiter'},
 {'sequence': '[CLS] the man worked as a barber. [SEP]',
  'score': 0.04962705448269844,
  'token': 13362,
  'token_str': 'barber'},
 {'sequence': '[CLS] the man worked as a mechanic. [SEP]',
  'score': 0.03788609802722931,
  'token': 15893,
  'token_str': 'mechanic'},
 {'sequence': '[CLS] the man worked as a salesman. [SEP]',
  'score': 0.037680890411138535,
  'token': 18968,
  'token_str': 'salesman'}]

>>> unmasker("The woman worked as a [MASK].")

[{'sequence': '[CLS] the woman worked as a nurse. [SEP]',
  'score': 0.21981462836265564,
  'token': 6821,
  'token_str': 'nurse'},
 {'sequence': '[CLS] the woman worked as a waitress. [SEP]',
  'score': 0.1597415804862976,
  'token': 13877,
  'token_str': 'waitress'},
 {'sequence': '[CLS] the woman worked as a maid. [SEP]',
  'score': 0.1154729500412941,
  'token': 10850,
  'token_str': 'maid'},
 {'sequence': '[CLS] the woman worked as a prostitute. [SEP]',
  'score': 0.037968918681144714,
  'token': 19215,
  'token_str': 'prostitute'},
 {'sequence': '[CLS] the woman worked as a cook. [SEP]',
  'score': 0.03042375110089779,
  'token': 5660,
  'token_str': 'cook'}]
```

This bias will also affect all fine-tuned versions of this model.

## Training data

The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).

## Training procedure

### Preprocessing

The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:

```
[CLS] Sentence A [SEP] Sentence B [SEP]
```

With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.

The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.

### Pretraining

The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.

## Evaluation results

When fine-tuned on downstream tasks, this model achieves the following results:

Glue test results:

| Task | MNLI-(m/mm) | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  | Average |
|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
|      | 84.6/83.4   | 71.2 | 90.5 | 93.5  | 52.1 | 85.8  | 88.9 | 66.4 | 79.6    |

### BibTeX entry and citation info

```bibtex
@article{DBLP:journals/corr/abs-1810-04805,
  author    = {Jacob Devlin and
               Ming{-}Wei Chang and
               Kenton Lee and
               Kristina Toutanova},
  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
               Understanding},
  journal   = {CoRR},
  volume    = {abs/1810.04805},
  year      = {2018},
  url       = {http://arxiv.org/abs/1810.04805},
  archivePrefix = {arXiv},
  eprint    = {1810.04805},
  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```


---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---

# DistilBERT base model (uncased)

This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was
introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found
[here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation). This model is uncased: it does
not make a difference between english and English.

## Model description

DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic
process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained
with three objectives:

- Distillation loss: the model was trained to return the same probabilities as the BERT base model.
- Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a
  sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the
  model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that
  usually see the words one after the other, or from autoregressive models like GPT which internally mask the future
  tokens. It allows the model to learn a bidirectional representation of the sentence.
- Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base
  model.

This way, the model learns the same inner representation of the English language than its teacher model, while being
faster for inference or downstream tasks.

## Intended uses & limitations

You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=distilbert) to look for
fine-tuned versions on a task that interests you.

Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.

### How to use

You can use this model directly with a pipeline for masked language modeling:

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")

[{'sequence': "[CLS] hello i'm a role model. [SEP]",
  'score': 0.05292855575680733,
  'token': 2535,
  'token_str': 'role'},
 {'sequence': "[CLS] hello i'm a fashion model. [SEP]",
  'score': 0.03968575969338417,
  'token': 4827,
  'token_str': 'fashion'},
 {'sequence': "[CLS] hello i'm a business model. [SEP]",
  'score': 0.034743521362543106,
  'token': 2449,
  'token_str': 'business'},
 {'sequence': "[CLS] hello i'm a model model. [SEP]",
  'score': 0.03462274372577667,
  'token': 2944,
  'token_str': 'model'},
 {'sequence': "[CLS] hello i'm a modeling model. [SEP]",
  'score': 0.018145186826586723,
  'token': 11643,
  'token_str': 'modeling'}]
```

Here is how to use this model to get the features of a given text in PyTorch:

```python
from transformers import DistilBertTokenizer, DistilBertModel
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertModel.from_pretrained("distilbert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

and in TensorFlow:

```python
from transformers import DistilBertTokenizer, TFDistilBertModel
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```

### Limitations and bias

Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions. It also inherits some of
[the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias).

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased')
>>> unmasker("The White man worked as a [MASK].")

[{'sequence': '[CLS] the white man worked as a blacksmith. [SEP]',
  'score': 0.1235365942120552,
  'token': 20987,
  'token_str': 'blacksmith'},
 {'sequence': '[CLS] the white man worked as a carpenter. [SEP]',
  'score': 0.10142576694488525,
  'token': 10533,
  'token_str': 'carpenter'},
 {'sequence': '[CLS] the white man worked as a farmer. [SEP]',
  'score': 0.04985016956925392,
  'token': 7500,
  'token_str': 'farmer'},
 {'sequence': '[CLS] the white man worked as a miner. [SEP]',
  'score': 0.03932540491223335,
  'token': 18594,
  'token_str': 'miner'},
 {'sequence': '[CLS] the white man worked as a butcher. [SEP]',
  'score': 0.03351764753460884,
  'token': 14998,
  'token_str': 'butcher'}]

>>> unmasker("The Black woman worked as a [MASK].")

[{'sequence': '[CLS] the black woman worked as a waitress. [SEP]',
  'score': 0.13283951580524445,
  'token': 13877,
  'token_str': 'waitress'},
 {'sequence': '[CLS] the black woman worked as a nurse. [SEP]',
  'score': 0.12586183845996857,
  'token': 6821,
  'token_str': 'nurse'},
 {'sequence': '[CLS] the black woman worked as a maid. [SEP]',
  'score': 0.11708822101354599,
  'token': 10850,
  'token_str': 'maid'},
 {'sequence': '[CLS] the black woman worked as a prostitute. [SEP]',
  'score': 0.11499975621700287,
  'token': 19215,
  'token_str': 'prostitute'},
 {'sequence': '[CLS] the black woman worked as a housekeeper. [SEP]',
  'score': 0.04722772538661957,
  'token': 22583,
  'token_str': 'housekeeper'}]
```

This bias will also affect all fine-tuned versions of this model.

## Training data

DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset
consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia)
(excluding lists, tables and headers).

## Training procedure

### Preprocessing

The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:

```
[CLS] Sentence A [SEP] Sentence B [SEP]
```

With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.

The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.

### Pretraining

The model was trained on 8 16 GB V100 for 90 hours. See the
[training code](https://github.com/huggingface/transformers/tree/master/examples/distillation) for all hyperparameters
details.

## Evaluation results

When fine-tuned on downstream tasks, this model achieves the following results:

Glue test results:

| Task | MNLI | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  |
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
|      | 82.2 | 88.5 | 89.2 | 91.3  | 51.3 | 85.8  | 87.5 | 59.9 |

### BibTeX entry and citation info

```bibtex
@article{Sanh2019DistilBERTAD,
  title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
  author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.01108}
}
```


---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---

# BERT base model (cased)

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference between
english and English.

Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
the Hugging Face team.

## Model description

BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:

- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
  the entire masked sentence through the model and has to predict the masked words. This is different from traditional
  recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
  GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
  sentence.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
  predict if the two sentences were following each other or not.

This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.

## Intended uses & limitations

You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
fine-tuned versions on a task that interests you.

Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.

### How to use

You can use this model directly with a pipeline for masked language modeling:

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-cased')
>>> unmasker("Hello I'm a [MASK] model.")

[{'sequence': "[CLS] Hello I'm a fashion model. [SEP]",
  'score': 0.09019174426794052,
  'token': 4633,
  'token_str': 'fashion'},
 {'sequence': "[CLS] Hello I'm a new model. [SEP]",
  'score': 0.06349995732307434,
  'token': 1207,
  'token_str': 'new'},
 {'sequence': "[CLS] Hello I'm a male model. [SEP]",
  'score': 0.06228214129805565,
  'token': 2581,
  'token_str': 'male'},
 {'sequence': "[CLS] Hello I'm a professional model. [SEP]",
  'score': 0.0441727414727211,
  'token': 1848,
  'token_str': 'professional'},
 {'sequence': "[CLS] Hello I'm a super model. [SEP]",
  'score': 0.03326151892542839,
  'token': 7688,
  'token_str': 'super'}]
```

Here is how to use this model to get the features of a given text in PyTorch:

```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = BertModel.from_pretrained("bert-base-cased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

and in TensorFlow:

```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = TFBertModel.from_pretrained("bert-base-cased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```

### Limitations and bias

Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions:

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-cased')
>>> unmasker("The man worked as a [MASK].")

[{'sequence': '[CLS] The man worked as a lawyer. [SEP]',
  'score': 0.04804691672325134,
  'token': 4545,
  'token_str': 'lawyer'},
 {'sequence': '[CLS] The man worked as a waiter. [SEP]',
  'score': 0.037494491785764694,
  'token': 17989,
  'token_str': 'waiter'},
 {'sequence': '[CLS] The man worked as a cop. [SEP]',
  'score': 0.035512614995241165,
  'token': 9947,
  'token_str': 'cop'},
 {'sequence': '[CLS] The man worked as a detective. [SEP]',
  'score': 0.031271643936634064,
  'token': 9140,
  'token_str': 'detective'},
 {'sequence': '[CLS] The man worked as a doctor. [SEP]',
  'score': 0.027423162013292313,
  'token': 3995,
  'token_str': 'doctor'}]

>>> unmasker("The woman worked as a [MASK].")

[{'sequence': '[CLS] The woman worked as a nurse. [SEP]',
  'score': 0.16927455365657806,
  'token': 7439,
  'token_str': 'nurse'},
 {'sequence': '[CLS] The woman worked as a waitress. [SEP]',
  'score': 0.1501094549894333,
  'token': 15098,
  'token_str': 'waitress'},
 {'sequence': '[CLS] The woman worked as a maid. [SEP]',
  'score': 0.05600163713097572,
  'token': 13487,
  'token_str': 'maid'},
 {'sequence': '[CLS] The woman worked as a housekeeper. [SEP]',
  'score': 0.04838843643665314,
  'token': 26458,
  'token_str': 'housekeeper'},
 {'sequence': '[CLS] The woman worked as a cook. [SEP]',
  'score': 0.029980547726154327,
  'token': 9834,
  'token_str': 'cook'}]
```

This bias will also affect all fine-tuned versions of this model.

## Training data

The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).

## Training procedure

### Preprocessing

The texts are tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form:

```
[CLS] Sentence A [SEP] Sentence B [SEP]
```

With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.

The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.

### Pretraining

The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.

## Evaluation results

When fine-tuned on downstream tasks, this model achieves the following results:

Glue test results:

| Task | MNLI-(m/mm) | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  | Average |
|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
|      | 84.6/83.4   | 71.2 | 90.5 | 93.5  | 52.1 | 85.8  | 88.9 | 66.4 | 79.6    |

### BibTeX entry and citation info

```bibtex
@article{DBLP:journals/corr/abs-1810-04805,
  author    = {Jacob Devlin and
               Ming{-}Wei Chang and
               Kenton Lee and
               Kristina Toutanova},
  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
               Understanding},
  journal   = {CoRR},
  volume    = {abs/1810.04805},
  year      = {2018},
  url       = {http://arxiv.org/abs/1810.04805},
  archivePrefix = {arXiv},
  eprint    = {1810.04805},
  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```


---
tags:
- exbert
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- no
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
---

# XLM-RoBERTa (base-sized model)

XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Conneau et al. and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/xlmr).

Disclaimer: The team releasing XLM-RoBERTa did not write a model card for this model so this model card has been written by the Hugging Face team.

## Model description

XLM-RoBERTa is a multilingual version of RoBERTa. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages.

RoBERTa is a transformers model pretrained on a large corpus in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.

More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.

This way, the model learns an inner representation of 100 languages that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the XLM-RoBERTa model as inputs.

## Intended uses & limitations

You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?search=xlm-roberta) to look for fine-tuned versions on a task that interests you.

Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation, you should look at models like GPT2.

## Usage

You can use this model directly with a pipeline for masked language modeling:

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='xlm-roberta-base')
>>> unmasker("Hello I'm a <mask> model.")

[{'score': 0.10563907772302628,
  'sequence': "Hello I'm a fashion model.",
  'token': 54543,
  'token_str': 'fashion'},
 {'score': 0.08015287667512894,
  'sequence': "Hello I'm a new model.",
  'token': 3525,
  'token_str': 'new'},
 {'score': 0.033413201570510864,
  'sequence': "Hello I'm a model model.",
  'token': 3299,
  'token_str': 'model'},
 {'score': 0.030217764899134636,
  'sequence': "Hello I'm a French model.",
  'token': 92265,
  'token_str': 'French'},
 {'score': 0.026436051353812218,
  'sequence': "Hello I'm a sexy model.",
  'token': 17473,
  'token_str': 'sexy'}]
```

Here is how to use this model to get the features of a given text in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')
model = AutoModelForMaskedLM.from_pretrained("xlm-roberta-base")

# prepare input
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')

# forward pass
output = model(**encoded_input)
```

### BibTeX entry and citation info

```bibtex
@article{DBLP:journals/corr/abs-1911-02116,
  author    = {Alexis Conneau and
               Kartikay Khandelwal and
               Naman Goyal and
               Vishrav Chaudhary and
               Guillaume Wenzek and
               Francisco Guzm{\'{a}}n and
               Edouard Grave and
               Myle Ott and
               Luke Zettlemoyer and
               Veselin Stoyanov},
  title     = {Unsupervised Cross-lingual Representation Learning at Scale},
  journal   = {CoRR},
  volume    = {abs/1911.02116},
  year      = {2019},
  url       = {http://arxiv.org/abs/1911.02116},
  eprinttype = {arXiv},
  eprint    = {1911.02116},
  timestamp = {Mon, 11 Nov 2019 18:38:09 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1911-02116.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```


YMCA in South Australia


The compound of philosophical radicalism, evangelical religion and self reliant ability typical of its founders had given an equalitarian flavour to South Australian thinking from the beginning.


It was into this social setting that in February 1850 a meeting was called primarily for the formation of an Association (apparently meaning a Y.M.C.A.)


for apprentices and others, after their day's work, to enjoy books, lectures, discussions, readings, friendly relief and recreation for a leisure hour.


Report 1851.


There was no census in 1850 but the 1851 census put the total population of South Australia at 63,700 with males numbering 35,302.


The discovery of Gold in Ballarat caused a large migration from South Australia and by 1852 some 8000 had left for the Goldfields.


As a consequence the various YMCA groups that had become established failed and by 1870 none remained.


i start to feel emotional


i believe feeling duality suffering soul growth tells of an ending or a decline or a change of direction often one associated with emotions and it offers one possible response to that decline or change moving on


i hostage negotiator on her case has her feeling hopeful about her future


im feeling a little dirty


i can cope with his presence without feeling distressed if i can force myself into a quiet and resigned friendship


i had the feeling that it might not have been taken as the truthful and sincere compliment it would have been


i asked some girls what it meant to them to be valued and for the most part the response was that they felt valued when the people around them made them feel valued and treated them in a loving and caring manner


i feel if i completely hated things i d exercise my democratic right speak my mind in what ever ways possible and try to enact a change


i could almost feel her gentle touch in the moonbeam she sent to shine over me he added touching his face dreamily


i woke up very early this morning feeling joyful


Our Deeds are the Reason of this #earthquake May ALLAH Forgive us all


Forest fire near La Ronge Sask. Canada


All residents asked to 'shelter in place' are being notified by officers. No other evacuation or shelter in place orders are expected


13,000 people receive #wildfires evacuation orders in California


Just got sent this photo from Ruby #Alaska as smoke from #wildfires pours into a school


#RockyFire Update => California Hwy. 20 closed in both directions due to Lake County fire - #CAfire #wildfires


#flood #disaster Heavy rain causes flash flooding of streets in Manitou, Colorado Springs areas


I'm on top of the hill and I can see a fire in the woods...


There's an emergency evacuation happening now in the building across the street


I'm afraid that the tornado is coming to our area...


In Rainbows was released over ten years ago.

In Rainbows

Alternative rock
art rock
experimental rock
art pop


Nigel Godrich has only produced one album.

In Rainbows


In Rainbows is less than 40 minutes long.

In Rainbows


It took over two years to record In Rainbows.

In Rainbows


In Rainbows falls into the art pop genre more than it does the art rock one.

In Rainbows


The run time of In Rainbows is over an hour.

In Rainbows


In Rainbows was released a few weeks before Halloween.

In Rainbows


In Rainbows had more than 10 tracks.

In Rainbows


In Rainbows was recorded in less than 60 days.

In Rainbows


Brooklyn has a run time under two hours.

Brooklyn

Saoirse Ronan
Domhnall Gleeson
Emory Cohen
Jim Broadbent
Julie Walters

BBC Films
Telefilm Canada
HanWay Films
Wildgaze Films

Fox Searchlight Pictures  (United States)

6 November 2015  ( 2015-11-06 )  (United Kingdom)


i bought lots and lots of items . Will you please show all of them


date of delivery of the order


May I know if u have exchange policy? I m not sure about the size of denim pant


this will be a good buy


What are the details of my order


why cannot i see my refund related details. show them


my order listing


can you please inform me on how much time my order will take


C ongressional employees are retained to perform public duties that include assisting Members in official responsibilities in personal, committee, leadership, or administrative office settings. The roles, duties, and activities of congressional staff are matters of ongoing interest to Members of Congress, congressional staff, groups, and individuals, including those who raise concerns about congressional operations. Most observers recognize that Congress does not function without staff, but there is little systematic attention to what staff do, or what Members expect of them. In congressional offices, there may be interest in identifying Member expectations of congressional staff duties by position from multiple perspectives, including assessment of staffing needs in Member offices; guidance in setting position expectations, qualifications, and experience when offices choose to hire staff; and informing current and potential congressional employees of position expectations. Members of the House and Senate generally establish their own employment policies and practices for their personal offices. It is arguably the case that within Member offices, a common group of activities is executed for which staff with relevant skillsets and other qualifications are necessary. A body of publicly available job advertisements for staff positions from a number of different offices can shed light on the expectations Members have for position duties, as well as staff skills, characteristics, experience, and other expectations. For 33 commonly used congressional staff position titles, this report describes the most frequently listed job duties, applicant skills, characteristics, prior experiences, and other expectations found in a sample of job advertisements placed by Members of Congress between approximately December 2014 and September 2019 seeking staff in their offices. Table 1 lists the position titles and the frequency with which advertisements for them appeared in the sample. Identifying Job Advertisements for Congressional Staff Positions Data used in developing sample position expectations were taken from several publicly available sources, including the following, over the periods specified: The House Employment Bulletin, published weekly by the House Vacancy Announcement and Placement Service (HVAPS) in the Human Resources Office of the House Chief Administrative Officer (CAO). Data were collected from ads published between approximately January 2015 and September 2019. The Employment Bulletin, published online by the Senate "as a service to Senate offices choosing to advertise staff vacancies." Data were collected from ads, which were not dated, appearing from approximately July 2016 to July 2019. The House GOP Job and Resume Bank, which posts ads on behalf of the House Republican Conference on Facebook. Ads were collected between approximately January and June 2017. Other ads were collected from the period between approximately December 2014 and January 2017 from the House GOP Job Bank web page on the website of Representative Virginia Foxx during part of her tenure as the House Republican Conference Secretary. The Job Announcements Board hosted by Representative Steny Hoyer during part of his tenure as House Minority Whip. Data were collected from ads posted between approximately January 2016 and December 2017. Categorizing and Coding Job Advertisements More than 1,800 ads were collected from all sources. Duplicate ads resulting from posts to more than one source, and ads that appear to have been frequently reposted, were removed, as were ads for positions in congressional settings other than personal offices, yielding 880 ads for positions in Member personal offices. Substantially similar position titles (e.g., deputy scheduler and state deputy scheduler) for which there were five or more ads were identified and grouped together, as were related job titles (e.g., positions designated as district, field, or regional representative that had essentially similar job duties and expectations) for which there were five or more substantially similar ads, yielding a total of 704 ads. Ads for the 33 identified position titles were further categorized if there were five or more ads that specified the advertised position as "not entry level" or other signifier of presumptive advanced status. The 704 ads were coded against a variety of variables within eight categories, including ad tracking information; ad details; position responsibilities and responsibility areas; expected job skills, qualifications, and credentials; application materials; and office type. The distribution of ads by job title and level is provided in Table 1 . Solicitations of applicants for congressional staff appear to originate in a highly decentralized manner. Means of identifying appropriate candidates might potentially include reassigning staff within offices, placing ads in services that make them available by subscription, word of mouth, and other nonpublic means of identifying potential applicants for congressional staff positions. Consequently, it cannot be determined whether the dataset of ads analyzed in this report is representative of all congressional employment solicitations. In addition, the process by which candidates for some Member office senior staff positions are identified may not be public-facing. Based on information specified within the ads, most position titles were identified by one of the following four primary responsibility areas (some positions were identified by up to three responsibility areas): Legislative, Policy, and Oversight, Media, Messaging, and Speeches, Constituent Communications, Outreach, and Service, and Office Administration and Support. For each position, at least one sample position description was created based on the coded data. Information includes the most frequently occurring of the following: primary responsibility areas; widely expected duties, typically up to six of the most frequently occurring duties specified in all ads for that position; other potential duties, typically up to six other duties mentioned in more than one ad; applicant information, including characteristics, skills, and knowledge and prior experience; and other expectations. Concluding Observations Categorizing congressional staff positions by position title relies on an assumption that similarly titled positions in House and Senate personal offices carry out the same tasks under essentially similar circumstances. While personal offices may carry out similar activities, the assumption might be questionable given the differences in staff resources in House and Senate offices, as well as potential differences within offices of each chamber. Generalizations about staff roles and duties may also be limited in some ways due to the broad discretion Members have with regard to running their office activities. Variations from office to office, which might include differences in job duties, work schedules, office emphases, and other factors, may limit the extent to which sample position expectations provided here match operational practices in all congressional offices. Sample Position Expectations Caseworker18 Communications Director19 Communications Director, "Senior Level" or "Not Entry Level"20 Constituent Services Representative21 Correspondence Manager22 Deputy Press Secretary23 Deputy Scheduler24 Deputy Scheduler/Assistant to Chief of Staff25 Digital Director/Press Assistant26 Digital Media Director27 District Director28 Executive Assistant29 Executive Assistant/Scheduler30 Executive Assistant/Scheduler, "Not Entry Level"31 Field, District, or Regional Representative32 Field Representative/Caseworker33 Legislative Aide34 Legislative Assistant35 Legislative Assistant, "Not Entry Level"36 Legislative Correspondent37 Legislative Correspondent/Press Assistant38 Legislative Correspondent/Staff Assistant39 Legislative Counsel40 Legislative Director, House41 Legislative Director "Senior Level," or "Not Entry Level"42 Legislative Director, Senate43 Military Legislative Assistant44 Press Assistant45 Press Secretary46 Regional Coordinator47 Scheduler48 Scheduler, "Not Entry Level"49 Scheduler/Office Manager50 Senior Legislative Assistant51 Speechwriter52 Staff Assistant53 Staff Assistant/Driver54 Staff Assistant/Press Assistant55 Systems Administrator56

The roles, duties, and activities of congressional staff are matters of ongoing interest to Members of Congress, congressional staff, and observers of Congress. Members of the House and Senate establish their own employment policies and practices for their personal offices. It is arguably the case that within Member offices, a common group of activities is executed for which staff are necessary. Accordingly, a group of job advertisements for those positions from a number of different offices can shed light on the expectations Members have for position duties, as well as staff skills, characteristics, experience, and other expectations. This report provides a set of 39 widely expected job duties, applicant skills, characteristics, prior experiences, and other expectations based on a sample of ads placed by Members of Congress between approximately December 2014 and September 2019 seeking staff in their offices for 33 position titles: Sample position expectations might assist Congress from multiple perspectives, including assessment of staffing needs in Member offices; guidance in setting position expectations, qualifications, and experience when offices need to hire staff; and informing current and potential congressional employees of position expectations. At the same time, categorizing congressional staff positions by position title relies on an assumption that similarly titled positions in House and Senate personal offices carry out the same tasks under essentially similar circumstances. Although personal offices may carry out similar activities, the assumption might be questionable given the differences in staff resources in House and Senate offices, as well as potential differences among offices of each chamber, particularly the Senate. Genera lizations about staff roles and duties may also be limited in some ways due to the broad discretion Members have with regard to running their office activities. Variations from office to office, which might include differences in job duties, work schedules, office emphases, and other factors, may limit the extent to which sample position expectations might match operational practices in all congressional offices. This is one of several CRS products on congressional staff. To access those products, see CRS Report R44688, Congressional Staff: CRS Products on Size, Pay, and Job Tenure .


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First report of glucose transporter 1 deficiency syndrome in Korea with a novel splice site mutation.

Glucose transporter type 1 deficiency syndrome (Glut-1DS) is caused by autosomal dominant haplodeficiency or autosomal recessive with homozygous mutation of the glucose transporter 1 (SLC2A1) gene and is characterized by severe seizures, developmental delay, ataxia and acquired microcephaly.

We describe the first known Korean patient with glucose transporter 1 deficiency syndrome, who had a novel mutation in the splice site.

The patient began having intractable seizures at 4 days of age that initially presented as eye blinking and apnea, evolving into generalized tonic seizures.

A lumbar puncture revealed low glucose concentration in the cerebrospinal fluid (CSF) in the setting of normoglycemia (blood glucose, 106 mg/dl; CSF glucose 21 mg/dl, and CSF to blood glucose ratio 0.20).

The results of a 3-O-methylglucose uptake study in erythrocytes (RBC) revealed that glucose uptake reduced to 48% of his parents in the patient.

The patient responded to a ketogenic diet that was initiated at 4 months of age and currently is on the modified Atkins diet (MAD) without seizures.

He does not require antiepileptic medication.

We diagnosed the first Glut-1 patient in Korea with a novel splice site mutation on the basis of clinical features, deficient glucose uptake and a mutation in the SLC2A1 gene.


Calculate the remainder when w is divided by 103.

Calculate the remainder when 163 is divided by y.
51
Suppose s - 6*s + 4*r = -16, -3*r = -3.

What is the remainder when 29 is divided by b?

What is the remainder when ((-336)/756)/(5/(-675)) is divided by 1 + (-1 - l)*15?

What is the remainder when 82 is divided by k?

What is the remainder when v is divided by u?

Calculate the remainder when x is divided by 93.

Calculate the remainder when 54 is divided by r.
0
Let q(x) = 139*x - 1104.

Calculate the remainder when j is divided by q(8).

Calculate the remainder when 616 is divided by w.
56
Let f = -49 - -98.

What is the remainder when k(6) is divided by f?

What is the remainder when l is divided by c?

10
Calculate the remainder when 45 is divided by 13 - (6 - -1) - (-6 - -4).

Let r = i - o. Calculate the remainder when 25 is divided by r.
11
Suppose -9416 = -s + 5*l, 2*s = -149*l + 156*l + 18844.

What is the remainder when s is divided by 33?

31
What is the remainder when 566 is divided by (-140)/(-56) - (-355)/10?

Calculate the remainder when r(-6) is divided by b.
1
Let h(a) = 289*a - 33.

Calculate the remainder when h(s) is divided by 86.

What is the remainder when h is divided by 138?

Calculate the remainder when l(4) is divided by 1/((-1)/(76/(-2))).

What is the remainder when v is divided by 6?

Calculate the remainder when i is divided by 10 + (-1 - (3 - 0)).

Calculate the remainder when 250 is divided by m.
90
Let i = -31 - -22.

Calculate the remainder when 45 is divided by b.
13
Suppose 19 = -2*k - 3*l, -4*l + 276 = 2*k + 304.

What is the remainder when 1072 is divided by k?

What is the remainder when m is divided by 5?

1
Calculate the remainder when 1051 is divided by -3*(-20)/(-15) - 651/(-31).

What is the remainder when 509 is divided by f?

Calculate the remainder when c is divided by 9.

What is the remainder when a is divided by c?

What is the remainder when j is divided by 15?

Calculate the remainder when 45 is divided by k.
1
Suppose -5*h = -3845 + 635.

What is the remainder when 4 + (-2)/((-12)/h) is divided by 28?

What is the remainder when w is divided by 21?

What is the remainder when 531 is divided by m(2)?

What is the remainder when 18/(h*10/(-175)) is divided by 26?

What is the remainder when g(-53) is divided by (-1)/(-4) + 62/8?

What is the remainder when 321 is divided by v?

What is the remainder when r is divided by 2?

What is the remainder when 53 is divided by y?

What is the remainder when p is divided by i?

41
Calculate the remainder when (1232/10)/(86/430) is divided by 69.

64
Let d(u) be the second derivative of -u**3/2 - 10*u**2 + 3*u + 16.

What is the remainder when 79 is divided by d(-22)?

Calculate the remainder when 94 is divided by z.
14
Let k(l) = -2*l**3 - 50*l**2 + 48*l - 98.

What is the remainder when p(9) is divided by k(-26)?

What is the remainder when 8475 is divided by m?

What is the remainder when u is divided by (-4)/16 - (-132)/16?

Calculate the remainder when 230 is divided by b.
54
Let m(s) = 54*s + 4895.

What is the remainder when m(0) is divided by 204?

What is the remainder when x is divided by 24?

20
Calculate the remainder when 192 is divided by (-510)/(-1360) - 429/(-8).

Calculate the remainder when 881 is divided by b.
1
Let n(a) = -a**3 - 8*a**2 - 94*a - 620.

Calculate the remainder when 2371 is divided by n(-8).

Calculate the remainder when v is divided by 47.

What is the remainder when 133 is divided by h?

Suppose 0 = 14*w - 22*w + u. What is the remainder when 13 is divided by w?

What is the remainder when b is divided by -4 - (1 + -2 + -10)?

Let m = 856 + g. Calculate the remainder when m is divided by 34.

What is the remainder when 22 is divided by g(j)?

What is the remainder when z is divided by 33?

Calculate the remainder when s is divided by o.
22
Suppose 168*v + 56*v - 38528 = 0.

What is the remainder when 1861 is divided by v?

What is the remainder when (-3)/(-1)*(41 + 2) is divided by k(7)?

30
What is the remainder when 1123 is divided by 124/6*8/(-48)*(1 + -10)?

Calculate the remainder when k is divided by 47.

What is the remainder when j is divided by 4?

What is the remainder when (332*1)/(2/1) is divided by 5/(90/84)*x?

Calculate the remainder when 286 is divided by g.
6
Let d(c) = 56*c**2 + 4*c - 4.

What is the remainder when n is divided by 19?

What is the remainder when r(-7) is divided by 34?

What is the remainder when 2198 is divided by g?

What is the remainder when b(3) is divided by 168?


3
What is the nearest to -0.3 in 1/4, -4/9, -5?

 (a) 1/8  (b) 2  (c) -148
a
Which is the nearest to -1?

0.2
What is the nearest to 0 in 3/22, -53, 1/4, 4?

 (a) 2  (b) -0.9  (c) 0.009  (d) 1
b
Which is the nearest to -11?

 (a) 2/7  (b) -27  (c) 1  (d) 1.2
d
Which is the nearest to 3/7?

 (a) -0.14  (b) 0.1  (c) 5/22
c
Which is the nearest to -19?

-4
What is the nearest to 0 in -1/5, 1/6, 4, -1/4?

 (a) 4/3  (b) -3  (c) 1/7  (d) -2/3
b
Which is the nearest to -9?

 (a) -0.4  (b) -1.5  (c) 0.2  (d) 5
a
Which is the nearest to 1?

 (a) -38  (b) -0.1  (c) 0.2  (d) 2
b
Which is the nearest to 4?

 (a) -2/13  (b) 24  (c) -0.2
a
Which is the nearest to 2?

 (a) 5  (b) 3  (c) -5  (d) 0.3
d
Which is the nearest to 3?

 (a) 0  (b) -2/13  (c) 1/3
c
Which is the nearest to 0?

 (a) 3  (b) 0  (c) -9  (d) -2/9
d
Which is the closest to -2/5?

-3/41
What is the nearest to 1/3 in 2, 3, -5, -0.3?

-0.3
What is the closest to -1 in 1, -0.4, -146?

2/11
What is the closest to -0.2 in -5, -51, -1?

 (a) 4/3  (b) -0.4  (c) 7
b
Which is the closest to -1/2?

 (a) -8/47  (b) 0  (c) 3
a
Which is the closest to 0?

 (a) 0.2  (b) 6/7  (c) -4
a
Which is the nearest to -1/4?

 (a) -1/12  (b) -0.2  (c) 231
b
Which is the nearest to 8/7?

1/5
What is the nearest to 0.1 in 6, -0.3, -22/7?

 (a) -0.4  (b) 5/2  (c) -1/12  (d) 3
a
Which is the nearest to -2?

 (a) 0.2  (b) 4  (c) -2.6  (d) 2
a
Which is the closest to -2/5?

 (a) 0.1  (b) -0.065  (c) 2
b
Which is the nearest to 4?

 (a) -2/9  (b) 4  (c) 17
b
Which is the nearest to -0.9?

 (a) -0.7  (b) 3  (c) 0
a
Which is the closest to 0.1?

 (a) -0.08  (b) -1  (c) 0.2  (d) -2
c
Which is the nearest to -5?

 (a) 1/2  (b) -1/6  (c) 4
c
Which is the closest to -1?

 (a) 2/3  (b) -5  (c) 2  (d) 2.298
a
Which is the nearest to 5.9?

 (a) -3  (b) -7  (c) 0.2  (d) 3/2
d
Which is the nearest to 0.05?

 (a) -1/4  (b) -4  (c) -5
a
Which is the nearest to -0.1?

 (a) -1  (b) -2/7  (c) 3/7
b
Which is the closest to 7?

 (a) -3/8  (b) 0.3  (c) 2  (d) 4/7
b
Which is the nearest to -0.1?

0.05
What is the closest to -2 in -4, -3, 1.1?

0.5
What is the closest to 3 in 16, 1, 12?

1
What is the nearest to 2/29 in 4, 3, 3/7?

3/7
What is the nearest to 1 in 3, -2, 0.3, -0.03?

0.3
What is the nearest to 0.3 in 5, -2, -15, -3?

-2/3
What is the closest to -61 in 2, -1/2, 4/13?

-0.3
What is the nearest to 2/7 in 1, 893, 0.2?

-0.3
What is the nearest to 2/31 in -2/9, -5, -2?

-1/7
What is the closest to 4 in 0.5, -1/3, -54/5?

 (a) 8/7  (b) 1  (c) 20
b
Which is the nearest to -0.3?

 (a) 9  (b) 5/2  (c) 2
c
Which is the closest to 3/4?

 (a) 0.3  (b) -3/2  (c) 0.1
b
Which is the closest to 0?

 (a) -2  (b) 7  (c) 0.5  (d) 4
a
Which is the nearest to 1?

 (a) 1  (b) -30  (c) 3/5  (d) 2
a
Which is the nearest to 6?

 (a) -1/3  (b) 2/3  (c) -6
b
Which is the nearest to 1?

 (a) -0.1  (b) 2  (c) 0.3
c
Which is the closest to -1/2?

 (a) 2  (b) -6  (c) -0.03
c
Which is the closest to 12?

 (a) 2/11  (b) 2  (c) 3  (d) -0.5
c
Which is the nearest to 0?

 (a) -1/3  (b) -3/7  (c) -2/5
a
Which is the closest to 7/4?

 (a) 0.205  (b) -0.3  (c) -4
b
Which is the closest to -1/4?


Essential guide to get driving license Dubai

Once you landed in Dubai you may required to obtain your driving license because of number of reasons.

That could be one of the essential document for your job(for example driver job), or you need a convenient transport mode for your family.

Whatever the needs you need to have valid license to drive vehicle in dubai or anywhere in UAE .

The below guide will give you details on how to go for it.

Criteria

Driving licences can be obtained by adult UAE citizens/residents after training from an authorized driving school in Dubai.

If you are 18-21 year of age, you can apply for a probationary licence.

If you have never driven before or have a driving licence that is not on the approved list of 36 countries then you must undergo training.

Documents Required for Driving License

You have to make ready below required documents to apply for driving licence in Dubai or UAE

Passport (original & copy) with residence visa stamp

No-Objection Certificate from your employer

8 photos white background

Eye test

Authorized Driving Schools in Dubai

You have to undergo driving training in any of the authorized training school by RTA.

Below are the authorized driving school in Dubai.

Dubai Driving license cost

Do you know the driving license in Dubai is a costly affair.

The cost for driving license will come around 3000 to 4000 dirhams if you pass in the first test.

This may vary in between driving school because they charging differently for per class.

For example 60 AED in Belhasa but it is 65AED in EDI.

Every fails will add up to 800 to 900 to this cost.

Suppose if a person pass in 4th test it will cost him approximately 6500/-.

You can check with driving school for exact amount.

Yeah as you think it is costly, but there is few ways we can you reduce this expenses.

As I am pointing out the cost saving tips here

Offers: You will get driving classes voucher from the deal websites like cobone or groupon websites.

So keep an eye on that, mostly the offer like it will give you 50% discount on first 10 classes.

Also same voucher being sent out by the people in Dubzzle online.

Early bird: focus to pass on first few tests or second time.

I have compiled few dubai driving license tips that helps you prepare for the same.

Get an eye test done either at an optician or at your driving school.

The school will handle all paper-work with RTA who may even have a counter at the school.

Receive your temporary driving licence, which you must carry in your training car.

Once you have passed all internal tests like garage and parking, your school will apply for your theory test followed by your road test.

The procedure to apply for a Driving license in Dubai

The first step on while applying Driving license, to get an eye test done by the Optometrist approved by RTA.

Mostly this done on the driving school itself.

Then your driving school will prepare an application form in Arabic and you need to submit to the RTA counter in the driving school itself.

You will receive a learner's license that valid for 6 months once your application success.

This process called file opening in RTA.

You will also receive a driver's handbook by RTA.

Theory Test

The theory test is carried out at the five authorized driving schools.

Don't forget to carry relevant ID, driving file+ 2 photos and theory test RTA fee of AED 200.

A total of 35 questions on road signals, hazards and guidelines on safe-driving will be asked in either English, Arabic or Urdu, with more languages to be added later.

Candidates who cannot read the touch-screen or are not computer literate can take the examination orally.

The first segment with 17 questions focuses on general traffic situations, and the candidate will need to answer a minimum of 11 correct to pass.

The second segment has 18 questions related to the specific driving license the candidate requires and a minimum of 12 correct answers are required to be eligible for the road test.

Road Test

On the date assigned, go with your passport, driving file+ 2 photos to the RTA Licence Section as advised by your driving school.

Fill up application form & submit fees (around AED 200 for road test).

When your name is called out, go along with the other three candidates and the RTA inspector to the designated car.

You will be given just a few minutes to demonstrate your driving and anticipatory skills.

Dubai Driving Test Tips
If you fail, you will have to register with your driving school for at least 7 more classes and get a new road-test date.

If you pass, collect the approval paper from your inspector and go to the Pass Counter.

Submit along with suitable ID & AED 100.

Collect your test file and submit to Control Counter.

After processing, pay required fee and proceed to photo area.

When your name is called out, have your photo taken.

In a few minutes, you will receive your DL and be eligible to drive in Dubai and the UAE.

For more information, please visit RTA website or call RTA on 8009090.


Catherine Cornille

Catherine Cornille (born 1961) is a professor of comparative theology and specializes in theology of religions and interreligious dialogue.

She presently holds the Newton College Alumnae Chair of Western Culture in the department of theology at Boston College.

Biography
With a previous B.A. from the KU Leuven and an M.A. from the University of Hawaii, Cornille returned to KU Leuven to complete her PhD in 1989.

She taught comparative religions at the KU Leuven and was the first woman professor in the history of the University.

She joined the department of theology at Boston College in 2005 and teaches comparative theology, theology of religions, and interreligious dialogue.

Works

References

Category:1961 births
Category:Living people
Category:Boston College faculty
Category:Religious studies scholars
Category:Women Christian theologians
Category:KU Leuven alumni
Category:University of Hawaii alumni


A boy is on a rock near calm waters taking a photograph of the scenery .

Two young guys with shaggy hair look at their hands while hanging out in the yard .


Two young , White males are outside near many bushes .


Two men in green shirts are standing in a yard .


A man in a blue shirt standing in a garden .


A man taking a photograph in a small body of water .


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Reconstructing Subject-Specific Effect Maps


Rotation Invariance Neural Network


Spherical polyharmonics and Poisson kernels for polyharmonic functions


A finite element approximation for the stochastic Maxwell--Landau--Lifshitz--Gilbert system


Comparative study of Discrete Wavelet Transforms and Wavelet Tensor Train decomposition to feature extraction of FTIR data of medicinal plants


On maximizing the fundamental frequency of the complement of an obstacle


On the rotation period and shape of the hyperbolic asteroid 1I/`Oumuamua (2017) U1 from its lightcurve


Adverse effects of polymer coating on heat transport at solid-liquid interface


SPH calculations of Mars-scale collisions: the role of the Equation of State, material rheologies, and numerical effects


$\mathcal{R}_{0}$ fails to predict the outbreak potential in the presence of natural-boosting immunity


Focusing on Cheaper Healthy Ingredients
Finding Deals on Healthy Groceries
Making Food at Home
Making Meal Plans

Not all healthy ingredients are expensive. In fact, many healthy ingredients are cheap. Take each category of food you need to buy, and figure out options in each one.  For instance, in whole grains, you can try oatmeal, brown rice, bulgur, popcorn, and whole-wheat bread and pasta. For vegetables, pick ones like cabbage, leafy greens (like mustard greens, kale, or even broccoli), squash, carrots, and celery. In fruit, go for cheaper options, such as oranges, apples, and bananas. In the dairy section, reach for milk and plain yogurt in large containers. Milk and yogurt can be flavored at home at minimal cost. Keep in mind that you probably need less protein than you think in your diet. Making your proteins last across several meals will help both your budget and your diet. For instance, if you make a chicken dish one night, use the leftovers the next night to make a chicken soup. The night after that, use some of the chicken in tacos.  Adult women under 30 only need 5 1/2 ounce-equivalents each day, while women over 30 only need 5 ounce-equivalents. Adult men under 30 need 6 1/2, while if you're 30 to 50, you need 6 ounce-equivalents and 5 1/2 if you're over 50. An "ounce-equivalent" is one ounce (28 grams) of meat (3 ounces of meat is about the size of a deck of cards). While you don't need to completely cut animal protein out of your diet, opting for plant-based protein sometimes can help cut costs. Try focusing meals around these vegetarian options, such as having beans and rice one night instead of chili.  Some vegetarian equivalents to an ounce of meat include an egg, 1/4 cup (60 milliliters) of beans, peas, or lentils, 1/2 an ounce (14 grams) of nuts or seeds, a tablespoon (15 milliliters) of peanut butter, or 2 tablespoons (30 milliliters) of hummus.  Extend your proteins with vegetables and complex carbohydrates. Combining a modest amount of meat with healthy extenders can make not only a healthier but also more filling meal. Consider:  Stir fry Tacos Pasta dishes While you need to compare prices on foods, you should also compare labels for items you're buying, particularly if you're buying prepacked food of any kind. For instance, if you must have a box of macaroni and cheese, it's best to pick the healthiest one you can find.  Look for foods low in sugar and sodium. You should be eating about 2,300 milligrams (1 teaspoon) of salt per day. Also, check for lower trans and saturated fat. Even the healthier fats should only be 20 to 30 percent of what you eat. Keep serving sizes to 400 calories or less. Also, check to see how many vitamins and minerals foods you purchase have.

When fruits and vegetables are in season, they tend to cost less. Often, grocery stores will run sales on what's in season because they know customers expect certain fruits and veggies at certain times of the year. As an added bonus, when things are in season, they taste better.  In fall, look for produce like pumpkins, winter squash, cabbage, and apples. In the winter, opt for winter squash, cabbage, and other root vegetables. Springtime is great for produce like beets, leafy greens, spring onions, and asparagus. Summer is a good time for watermelon, corn, and berries, just to name a few. You'll likely notice the cheapest produce prices in summer. Also, because produce is cheaper this time of year, you can buy extra and freeze it or can it yourself. While you may prefer fresh fruits and vegetables, you can often save money by opting for frozen or canned instead. These options have the same health benefits, though be sure to pick ones that don't have added sugar or salt. Check your proteins, too. Frozen chicken may be cheaper than fresh, and canned salmon and tuna is likely cheaper than fresh fish. Most grocery stores run weekly specials, which you can learn about through their weekly ads. When something is on sale, that's the time to stock up on it. Most protein can be frozen and saved for later, for instance, so if your store is running a sale on boneless, skinless chicken breasts, buy some to use and save for later. You probably already know to check for the lowest prices on the items you're buying. However, you may get stuck in a rut buying a certain brand and assuming it's the cheapest because it always has been. However, be vigilant, as prices change all the time. Occasionally there is a sale on a certain package size, or a discount on a run of product that changes what is the most affordable option.   Look up and down, as grocery stores tend to put the most expensive items at eye level. Keep an eye on store brands-- they tend to be cheaper. While buying in bulk can come with a higher price upfront, it'll be cheaper overall. For instance, buying a large can of quick oatmeal is much cheaper than buying a box of the instant packets. Add some fresh fruit to create your own flavors.  You can also buy some items in bulk bins at health food stores. Often these stores will have items like grains, beans, pastas, nuts, granolas, flours, and sugars. You can get just what you need, taking advantage of the cheaper prices without having to buy more than you need. Bulk items require timely use. Do not fall into the trap of buying an entire gallon of mayonnaise that you will never use up before it expires, or that bargain box of cereal that no one in the family will eat. Take advantage of coupons when you come across them. However, only use them for the foods you would buy anyway. If you're buying something you don't normally buy--even if it is inexpensive--you may be spending extra money for a product you do not want. You can find coupons online, as well as in the newspaper. You can also use a variety of coupon apps to save money. If you are low-income, you might qualify for SNAP or WIC. SNAP is the contemporary version of food stamps, and you get your assistance on a debit-type card. WIC is only for women with low income who have young children. Pregnant women also qualify. It's also more restrictive about what you can buy, but it can still help you afford healthy food.  Visit your local SNAP office to apply in person, or you can apply online in many states. Check if you can apply online in your state at https://www.fns.usda.gov/snap/apply. To apply for WIC, contact your local WIC office. Sometimes, you can find cheaper produce at the farmer's market, though you must hunt for deals. Also, driving out to local farms to pick your own produce can make it cheaper, as well. Be ready to use what you buy, though, as farm-fresh produce doesn't tend to last as long as grocery market produce, which is bred and treated to last longer. However, you'll make up for it in flavor. Some farmer's markets even accept SNAP.

Often, having a small garden or even a few potted plants can be a cheap way to get in the veggies you need. Try growing a tomato plant, for instance, or even just a few herbs on your window sill. You can buy pre-made snacks at the store that are a bit healthier than your average chips, such as veggie chips or fruit cups. However, they tend to be expensive, so making your own at home is a more affordable alternative.  For example, try making kale chips. Wash and dry kale thoroughly. Chop or tear the leaves into large pieces. Toss them in olive oil, or spray them with cooking spray, and then spread them out on a large baking sheet in a single layer. Sprinkle salt and pepper on top and any other seasoning you want. Bake them at 350 degrees Fahrenheit (177 Celsius) until crispy (about 15 minutes). You can also make yourself individual cups of fruits and veggies. For instance, cut up oranges, apples, and grapefruit, and toss them with a tiny bit of honey. Spoon them into small reusable containers so you can grab them any time. You can do the same with veggies. Cut them into bite-sized pieces, and put them in reusable bags. Spoon out (homemade) hummus into individual containers to go with them. Stocks and broths are a great way to start out soups, but buying them in cans or cartons can get expensive. Plus, they're often loaded with extra sodium. Making your own at home results in better quality, and it's cheaper, too.  You can even make a stock with your scraps. Save the bits of vegetables you have leftover, such as onion skins, the ends of carrots, and the tops of celery. When you eat a chicken, save the bones and the bits of meat. Freeze them in a bag until you have enough. When you do, throw them in a pot and cover them with water. Boil them (covered on low heat) for six to eight hours or until you have a stock with a flavor you like. Strain it, and your stock is ready. Once you have your stock, you can portion it out in small containers and freeze it. Lots of foods besides snacks and broths can be made cheaper at home. For instance, if you eat yogurt often, making your own yogurt might be a good option. For bread, consider investing in a bread making machine, so you can make your own bread at the push of a button.

Making a meal plan will help you to only buy what you need and therefore stay under budget. If you don't already have healthy recipes you love, find some you like online, do some research with recipes books, or ask your friends for recommendations.  One site you can try is the USDA's What's Cooking website (https://whatscooking.fns.usda.gov/). It has healthy recipes that you can use to plan meals. Don't forget to plan around your schedule. Opt for leftovers or quick meals on nights you're going to be busy. You may think that eating healthy means you need to try out a bunch of new trendy health foods. While these trendy foods may be healthy, that doesn't mean they're the only healthy foods. Stick to simpler ingredients that you know, even something as simple as a roasted chicken with vegetables and brown rice. You'll be more likely to keep eating healthy, and you'll save money at the same time. Once you've planned out your meals, create a list of the items you'll need. Sticking to a list (and not buying extras) can help you stay on budget, so you can afford healthier food. Meal planning isn't just for eating at home. It can help to make a plan before you go to a restaurant. Look over the menu if it's available online, and compare calorie counts. Many restaurants have the calorie counts available, but you can also use online calorie counters or websites.  Consider choosing a healthy appetizer to save money. It will also keep the portion small. Opt for dishes with lean proteins paired with whole grains and lots of veggies. Choose veggies or fruit over fries, onion rings, or mashed potatoes. Ask for a take-out box when you get your food. Split it in half before you start eating, and put half in the box to take home. Splitting it up helps you control your portion and stretch your money.

Summary: Choose cheaper ingredients. Make your proteins last longer. Go vegetarian. Read labels.

Summary: Pick what's in season. Check the prices on frozen and canned. Check out the weekly deals. Compare prices every time. Buy in bulk. Use coupons. Apply for SNAP or WIC. Find a farmer's market or local farms.

Summary: Grow what you can. Create your own snacks. Boil up your own stocks and broths. Branch out into making other foods.

Summary: Decide on your main meals for the week. Stick with what you know. Create your shopping list. Plan ahead when you eat out.


Trying Work and Lifestyle Modifications
Trying Eye Exercises

Since the main culprit for eye strain is today's culture is time spent in front of a computer, a TV, a cell phone, or another device with a screen, finding alternatives such as reading a paper book rather than reading on your iPad can be a huge help. Also, to reduce the harmful effects of screen time that cannot be avoided (such as on your computer at work), try positioning the screen lower and using an anti-glare screen to diminish the strain on your eyes. If you are having continued eye strain that is bothering you in your day-to-day life, consult your physician. It is possible that you have a vision problem, or another eye condition, that may be contributing to the discomfort and/or tension you feel. It is always better to be safe rather than sorry, and to see a doctor to ensure that nothing more serious is going on (as well as to obtain appropriate medical treatment if needed).

Do this sitting up to prevent yourself from falling asleep. Close them as tightly as you can to help your eyes relax.  Hold this position for a few seconds, and then quickly open your eyes. Repeat it three to five times to help your eyes relax. After doing it quickly a few times, close your eyes very tightly and hold the pose for about a minute. Focus on your breathing to help you relax even further.

Summary:
Limit screen time. See a doctor.
Close your eyes.


There is no relation at all between Fortier and Profiler but the fact that both are police series about violent crimes. Profiler looks crispy, Fortier looks classic. Profiler plots are quite simple. Fortier's plot are far more complicated... Fortier looks more like Prime Suspect, if we have to spot similarities... The main character is weak and weirdo, but have "clairvoyance". People like to compare, to judge, to evaluate. How about just enjoying? Funny thing too, people writing Fortier looks American but, on the other hand, arguing they prefer American series (!!!). Maybe it's the language, or the spirit, but I think this series is more English than American. By the way, the actors are really good and funny. The acting is not superficial at all...


This movie is a great. The plot is very true to the book which is a classic written by Mark Twain. The movie starts of with a scene where Hank sings a song with a bunch of kids called "when you stub your toe on the moon" It reminds me of Sinatra's song High Hopes, it is fun and inspirational. The Music is great throughout and my favorite song is sung by the King, Hank (bing Crosby) and Sir "Saggy" Sagamore. OVerall a great family movie or even a great Date movie. This is a movie you can watch over and over again. The princess played by Rhonda Fleming is gorgeous. I love this movie!! If you liked Danny Kaye in the Court Jester then you will definitely like this movie.


George P. Cosmatos' "Rambo: First Blood Part II" is pure wish-fulfillment. The United States clearly didn't win the war in Vietnam. They caused damage to this country beyond the imaginable and this movie continues the fairy story of the oh-so innocent soldiers. The only bad guys were the leaders of the nation, who made this war happen. The character of Rambo is perfect to notice this. He is extremely patriotic, bemoans that US-Americans didn't appreciate and celebrate the achievements of the single soldier, but has nothing but distrust for leading officers and politicians. Like every film that defends the war (e.g. "We Were Soldiers") also this one avoids the need to give a comprehensible reason for the engagement in South Asia. And for that matter also the reason for every single US-American soldier that was there. Instead, Rambo gets to take revenge for the wounds of a whole nation. It would have been better to work on how to deal with the memories, rather than suppressing them. "Do we get to win this time?" Yes, you do.


In the process of trying to establish the audiences' empathy with Jake Roedel (Tobey Maguire) the filmmakers slander the North and the Jayhawkers. Missouri never withdrew from the Union and the Union Army was not an invading force. The Southerners fought for State's Rights: the right to own slaves, elect crooked legislatures and judges, and employ a political spoils system. There's nothing noble in that. The Missourians could have easily traveled east and joined the Confederate Army.

It seems to me that the story has nothing to do with ambiguity. When Jake leaves the Bushwhackers, it's not because he saw error in his way, he certainly doesn't give himself over to the virtue of the cause of abolition.