Instructions to use fmnxl/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fmnxl/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fmnxl/output")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("fmnxl/output") model = AutoModelForTokenClassification.from_pretrained("fmnxl/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +10 -10
- model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mmBERT-base) on the None dataset.
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It achieves the following results on the evaluation set:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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### Framework versions
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This model is a fine-tuned version of [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mmBERT-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4235
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- Precision: 0.7559
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- Recall: 0.7428
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- F1: 0.7493
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- Accuracy: 0.8486
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.4152 | 1.0 | 477 | 0.4235 | 0.7559 | 0.7428 | 0.7493 | 0.8486 |
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| 0.3653 | 2.0 | 954 | 0.3950 | 0.7364 | 0.7418 | 0.7391 | 0.8577 |
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| 0.3508 | 3.0 | 1431 | 0.3991 | 0.7602 | 0.7371 | 0.7485 | 0.8577 |
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| 0.2844 | 4.0 | 1908 | 0.4244 | 0.7328 | 0.7445 | 0.7386 | 0.8543 |
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| 0.2143 | 5.0 | 2385 | 0.4813 | 0.7324 | 0.7449 | 0.7386 | 0.8507 |
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### Framework versions
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model.safetensors
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training_args.bin
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