Text Classification
Transformers
Safetensors
English
xlnet
automatic-short-answer-grading
regression
education
short-answer
assessment
grading
Eval Results (legacy)
Instructions to use kenzykhaled/XLENT_ASAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kenzykhaled/XLENT_ASAG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kenzykhaled/XLENT_ASAG")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kenzykhaled/XLENT_ASAG") model = AutoModelForSequenceClassification.from_pretrained("kenzykhaled/XLENT_ASAG") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "xlnet-base-cased", | |
| "architectures": [ | |
| "XLNetForSequenceClassification" | |
| ], | |
| "attn_type": "bi", | |
| "bi_data": false, | |
| "bos_token_id": 1, | |
| "clamp_len": -1, | |
| "d_head": 64, | |
| "d_inner": 3072, | |
| "d_model": 768, | |
| "dropout": 0.1, | |
| "end_n_top": 5, | |
| "eos_token_id": 2, | |
| "ff_activation": "gelu", | |
| "id2label": { | |
| "0": "LABEL_0" | |
| }, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "LABEL_0": 0 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "mem_len": null, | |
| "model_type": "xlnet", | |
| "n_head": 12, | |
| "n_layer": 12, | |
| "pad_token_id": 5, | |
| "problem_type": "regression", | |
| "reuse_len": null, | |
| "same_length": false, | |
| "start_n_top": 5, | |
| "summary_activation": "tanh", | |
| "summary_last_dropout": 0.1, | |
| "summary_type": "last", | |
| "summary_use_proj": true, | |
| "task_specific_params": { | |
| "text-generation": { | |
| "do_sample": true, | |
| "max_length": 250 | |
| } | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.48.3", | |
| "untie_r": true, | |
| "use_mems_eval": true, | |
| "use_mems_train": false, | |
| "vocab_size": 32000 | |
| } | |