Text Classification
Transformers
Safetensors
distilbert
emotion-classification
text-embeddings-inference
Instructions to use hamzawaheed/emotion-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hamzawaheed/emotion-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hamzawaheed/emotion-classification-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hamzawaheed/emotion-classification-model") model = AutoModelForSequenceClassification.from_pretrained("hamzawaheed/emotion-classification-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hamzawaheed/emotion-classification-model
Browse files- README.md +4 -4
- model.safetensors +1 -1
README.md
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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### Framework versions
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1941
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- Accuracy: 0.926
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| 0.2211 | 1.0 | 500 | 0.2383 | 0.915 |
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| 0.1274 | 2.0 | 1000 | 0.1941 | 0.926 |
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### Framework versions
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model.safetensors
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