<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of distilbert-base-uncased on the clinc_oos dataset. It achieves the following results on the evaluation set:
- Loss: 0.3215
- Accuracy: 0.9458
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 384
- eval_batch_size: 384
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 1.0 | 40 | 3.4217 | 0.1958 |
No log | 2.0 | 80 | 2.9574 | 0.6029 |
No log | 3.0 | 120 | 2.4741 | 0.7294 |
No log | 4.0 | 160 | 2.0307 | 0.7926 |
No log | 5.0 | 200 | 1.6384 | 0.8339 |
No log | 6.0 | 240 | 1.3096 | 0.8658 |
No log | 7.0 | 280 | 1.0421 | 0.8974 |
2.2117 | 8.0 | 320 | 0.8340 | 0.9126 |
2.2117 | 9.0 | 360 | 0.6804 | 0.9216 |
2.2117 | 10.0 | 400 | 0.5784 | 0.93 |
2.2117 | 11.0 | 440 | 0.5109 | 0.9348 |
2.2117 | 12.0 | 480 | 0.4665 | 0.9387 |
2.2117 | 13.0 | 520 | 0.4352 | 0.9387 |
2.2117 | 14.0 | 560 | 0.4149 | 0.9406 |
2.2117 | 15.0 | 600 | 0.3973 | 0.9439 |
0.4743 | 16.0 | 640 | 0.3867 | 0.9429 |
0.4743 | 17.0 | 680 | 0.3793 | 0.9426 |
0.4743 | 18.0 | 720 | 0.3732 | 0.9435 |
0.4743 | 19.0 | 760 | 0.3649 | 0.9445 |
0.4743 | 20.0 | 800 | 0.3583 | 0.9455 |
0.4743 | 21.0 | 840 | 0.3577 | 0.9448 |
0.4743 | 22.0 | 880 | 0.3520 | 0.9432 |
0.4743 | 23.0 | 920 | 0.3488 | 0.9465 |
0.2577 | 24.0 | 960 | 0.3470 | 0.9458 |
0.2577 | 25.0 | 1000 | 0.3434 | 0.9471 |
0.2577 | 26.0 | 1040 | 0.3427 | 0.9465 |
0.2577 | 27.0 | 1080 | 0.3407 | 0.9452 |
0.2577 | 28.0 | 1120 | 0.3389 | 0.9461 |
0.2577 | 29.0 | 1160 | 0.3377 | 0.9465 |
0.2577 | 30.0 | 1200 | 0.3380 | 0.9458 |
0.2577 | 31.0 | 1240 | 0.3338 | 0.9461 |
0.219 | 32.0 | 1280 | 0.3348 | 0.9465 |
0.219 | 33.0 | 1320 | 0.3334 | 0.9461 |
0.219 | 34.0 | 1360 | 0.3314 | 0.9468 |
0.219 | 35.0 | 1400 | 0.3290 | 0.9481 |
0.219 | 36.0 | 1440 | 0.3292 | 0.9477 |
0.219 | 37.0 | 1480 | 0.3296 | 0.9458 |
0.219 | 38.0 | 1520 | 0.3290 | 0.9461 |
0.219 | 39.0 | 1560 | 0.3267 | 0.9458 |
0.2039 | 40.0 | 1600 | 0.3281 | 0.9458 |
0.2039 | 41.0 | 1640 | 0.3272 | 0.9458 |
0.2039 | 42.0 | 1680 | 0.3245 | 0.9468 |
0.2039 | 43.0 | 1720 | 0.3260 | 0.9461 |
0.2039 | 44.0 | 1760 | 0.3244 | 0.9455 |
0.2039 | 45.0 | 1800 | 0.3245 | 0.9458 |
0.2039 | 46.0 | 1840 | 0.3243 | 0.9455 |
0.2039 | 47.0 | 1880 | 0.3235 | 0.9455 |
0.1965 | 48.0 | 1920 | 0.3228 | 0.9455 |
0.1965 | 49.0 | 1960 | 0.3232 | 0.9465 |
0.1965 | 50.0 | 2000 | 0.3228 | 0.9461 |
0.1965 | 51.0 | 2040 | 0.3232 | 0.9468 |
0.1965 | 52.0 | 2080 | 0.3220 | 0.9461 |
0.1965 | 53.0 | 2120 | 0.3211 | 0.9465 |
0.1965 | 54.0 | 2160 | 0.3217 | 0.9458 |
0.1965 | 55.0 | 2200 | 0.3219 | 0.9465 |
0.1929 | 56.0 | 2240 | 0.3216 | 0.9461 |
0.1929 | 57.0 | 2280 | 0.3218 | 0.9465 |
0.1929 | 58.0 | 2320 | 0.3212 | 0.9458 |
0.1929 | 59.0 | 2360 | 0.3214 | 0.9455 |
0.1929 | 60.0 | 2400 | 0.3215 | 0.9458 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.14.1