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kennethge123/entailed_after_rte-gpt2
entailed_after_rte-gpt2 is a text classification model from kennethge123. Use it when you need a label for a piece of text. 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 kennethge123/superglue_rte-gpt2 on the bigbench dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 30 | 0.9025 | 0.3571 |
| No log | 2.0 | 60 | 0.7155 | 0.5 |
| No log | 3.0 | 90 | 1.0014 | 0.2857 |
| No log | 4.0 | 120 | 0.9748 | 0.5714 |
| No log | 5.0 | 150 | 0.9511 | 0.5714 |
| No log | 6.0 | 180 | 1.0164 | 0.6429 |
| No log | 7.0 | 210 | 1.6015 | 0.5 |
| No log | 8.0 | 240 | 1.2833 | 0.6429 |
| No log | 9.0 | 270 | 1.0093 | 0.7857 |
| No log | 10.0 | 300 | 1.6339 | 0.6429 |
| No log | 11.0 | 330 | 1.3461 | 0.5714 |
| No log | 12.0 | 360 | 1.2949 | 0.6429 |
| No log | 13.0 | 390 | 1.6343 | 0.6429 |
| No log | 14.0 | 420 | 0.8418 | 0.8571 |
| No log | 15.0 | 450 | 0.6750 | 0.8571 |
| No log | 16.0 | 480 | 2.0221 | 0.6429 |
| 0.5929 | 17.0 | 510 | 0.7579 | 0.8571 |
| 0.5929 | 18.0 | 540 | 1.5713 | 0.7143 |
| 0.5929 | 19.0 | 570 | 1.0489 | 0.7143 |
| 0.5929 | 20.0 | 600 | 1.1865 | 0.7143 |