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theekshana/deid-roberta-i2b2-NER-medical-reports
deid-roberta-i2b2-NER-medical-reports is a token classification model from theekshana. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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From the Hugging Face model README
This model is a fine-tuned version of obi/deid_roberta_i2b2 on the None dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|---|
| 0.4678 | 1.0 | 125 | 0.4248 | 0.0061 | 0.7737 | 0.8229 | 0.7836 | 0.8229 |
| 0.3877 | 2.0 | 250 | 0.4008 | 0.0061 | 0.7886 | 0.8282 | 0.8060 | 0.8282 |
| 0.3391 | 3.0 | 375 | 0.3132 | 0.0061 | 0.8213 | 0.8672 | 0.8389 | 0.8672 |
| 0.3091 | 4.0 | 500 | 0.3124 | 0.0061 | 0.8334 | 0.8597 | 0.8419 | 0.8597 |
| 0.2572 | 5.0 | 625 | 0.2570 | 0.0061 | 0.8675 | 0.8911 | 0.8739 | 0.8911 |
| 0.2368 | 6.0 | 750 | 0.2270 | 0.0061 | 0.8908 | 0.9084 | 0.8973 | 0.9084 |
| 0.2115 | 7.0 | 875 | 0.2219 | 0.0061 | 0.8960 | 0.9081 | 0.9017 | 0.9081 |
| 0.1949 | 8.0 | 1000 | 0.2325 | 0.0061 | 0.8993 | 0.9044 | 0.8991 | 0.9044 |
| 0.1843 | 9.0 | 1125 | 0.2218 | 0.0061 | 0.9035 | 0.9103 | 0.9059 | 0.9103 |
| 0.1691 | 10.0 | 1250 | 0.2126 | 0.0061 | 0.9143 | 0.9156 | 0.9132 | 0.9156 |