Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use schoenml/bert-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use schoenml/bert-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="schoenml/bert-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("schoenml/bert-emotion") model = AutoModelForSequenceClassification.from_pretrained("schoenml/bert-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 2
Browse files
last-checkpoint/optimizer.pt
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last-checkpoint/pytorch_model.bin
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last-checkpoint/rng_state.pth
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last-checkpoint/scheduler.pt
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last-checkpoint/trainer_state.json
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"max_steps": 2445,
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"best_metric": 0.7324328527827817,
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"best_model_checkpoint": "bert-emotion/checkpoint-1630",
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"epoch": 2.0,
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"global_step": 1630,
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"eval_samples_per_second": 392.021,
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"eval_loss": 0.9663205146789551,
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"eval_precision": 0.7584143950163228,
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"eval_recall": 0.7196118817328854,
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"eval_runtime": 0.969,
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"eval_samples_per_second": 385.981,
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"step": 1630
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pytorch_model.bin
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