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IrisCHEN04/hedge_detection_distill_bert
hedge_detection_distill_bert is a machine learning model from IrisCHEN04. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Please be careful when using the model. As a linguist, I do not think it meet my standards. But it may be OK for you if you are not from my field and do not need to be that prudent on the concept.
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From the Hugging Face model README
Please be careful when using the model. As a linguist, I do not think it meet my standards. But it may be OK for you if you are not from my field and do not need to be that prudent on the concept.
I trained it for 5 epochs using the below dataset:
Farkas, R., Vincze, V., Móra, G., Csirik, J., & Szarvas, G. (2010). The CoNLL-2010 shared task: Learning to detect hedges and their scope in natural language text. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning—Shared Task (pp. 1-12). Association for Computational Linguistics.
| Metric | Value |
|---|---|
| Global Step | 2,895 |
| Training Loss | 0.0056 |
| Epochs | 5.0 |
| Training Runtime | 110.40 seconds |
| Train Samples/Second | 419.40 |
| Train Steps/Second | 26.22 |
| Total FLOPs | 9.53 × 10¹⁴ |
| Epoch | Training Loss | Validation Loss | Precision | Recall | F1 Score | Accuracy |
|---|---|---|---|---|---|---|
| 1 | 0.0181 | 0.0075 | 0.8882 | 0.8773 | 0.8827 | 0.9975 |
| 2 | 0.0053 | 0.0070 | 0.8711 | 0.9018 | 0.8862 | 0.9975 |
| 3 | 0.0044 | 0.0076 | 0.9141 | 0.8972 | 0.9056 | 0.9980 |
| 4 | 0.0024 | 0.0089 | 0.8880 | 0.9003 | 0.8941 | 0.9977 |
| 5 | 0.0018 | 0.0100 | 0.8965 | 0.9034 | 0.8999 | 0.9978 |