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
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README.md
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# output
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This model is a fine-tuned version of [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mmBERT-base) on
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It achieves the following results on the evaluation set:
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- Accuracy: 0.
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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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# output
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This model is a fine-tuned version of [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mmBERT-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6104
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- Precision: 0.6530
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- Recall: 0.6880
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- F1: 0.6700
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- Accuracy: 0.7837
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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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| 0.6422 | 1.0 | 971 | 0.6301 | 0.6543 | 0.6677 | 0.6609 | 0.7806 |
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| 0.6043 | 2.0 | 1942 | 0.6104 | 0.6530 | 0.6880 | 0.6700 | 0.7837 |
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| 0.5953 | 3.0 | 2913 | 0.6006 | 0.6385 | 0.6841 | 0.6605 | 0.7800 |
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| 0.5578 | 4.0 | 3884 | 0.6148 | 0.6517 | 0.6811 | 0.6661 | 0.7831 |
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| 0.4908 | 5.0 | 4855 | 0.6539 | 0.6385 | 0.6655 | 0.6518 | 0.7723 |
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### Framework versions
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model.safetensors
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training_args.bin
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