Text Classification
Transformers
Safetensors
English
distilbert
intel
ipex
bf16
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("rrpetroff/emotion-bot-1000")
model = AutoModelForSequenceClassification.from_pretrained("rrpetroff/emotion-bot-1000")Quick Links
Model Card for Model ID
Model Description
- Purpose: Classifying emotions from text.
- Model architecture: Distilbert
- Training data: Text examples with labels of corresponding emotions ex: sad, happy, love, etc.
Intended Use
- Intended users: For classifying the sentiment of text from examples provided.
- Use cases: Retail, social media, etc.
Limitations
- Known limitations: N/A
Hardware
- Training Platform: Intel 4th Generation Xeon Processors on the Intel Developer Cloud
Software Optimizations
- Known Optimizations:
- Intel Extension for PyTorch
- Mixed Precision Training (FP32 and BF16)
- Advanced Matrix Extensions (in Xeon Processor)
Ethical Considerations
- Ethical concerns: Beware of potential erroneous results when applying to usecases that involve humans.
More Information
- [Include any additional information, links, or references.]
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rrpetroff/emotion-bot-1000")