Text Classification
Transformers
Safetensors
PyTorch
English
Hindi
distilbert
emotion-detection
sentiment-analysis
mental-health
emotion-classification
hinglish
Eval Results (legacy)
text-embeddings-inference
Instructions to use Fynman-stack/raven-emotion-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fynman-stack/raven-emotion-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fynman-stack/raven-emotion-distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fynman-stack/raven-emotion-distilbert") model = AutoModelForSequenceClassification.from_pretrained("Fynman-stack/raven-emotion-distilbert") - Notebooks
- Google Colab
- Kaggle
Soumyadip Raha
Upload Raven emotion classifier - fine-tuned DistilBERT (97.62% accuracy on personal data)
4f4c5ba verified | { | |
| "backend": "tokenizers", | |
| "cls_token": "[CLS]", | |
| "do_lower_case": true, | |
| "is_local": true, | |
| "mask_token": "[MASK]", | |
| "max_length": 128, | |
| "model_max_length": 512, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "[PAD]", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "sep_token": "[SEP]", | |
| "stride": 0, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "DistilBertTokenizer", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "[UNK]" | |
| } | |