metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model
results: []
finetuning-sentiment-model
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4498
- Accuracy: 0.9279
- F1: 0.9283
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: 512
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
No log | 1.0 | 49 | 0.2333 | 0.9086 | 0.9090 |
No log | 2.0 | 98 | 0.2126 | 0.9167 | 0.9193 |
No log | 3.0 | 147 | 0.2129 | 0.9185 | 0.9213 |
No log | 4.0 | 196 | 0.2369 | 0.9110 | 0.9155 |
No log | 5.0 | 245 | 0.2155 | 0.9267 | 0.9270 |
No log | 6.0 | 294 | 0.2311 | 0.9258 | 0.9259 |
No log | 7.0 | 343 | 0.2463 | 0.926 | 0.9261 |
No log | 8.0 | 392 | 0.2757 | 0.9237 | 0.9252 |
No log | 9.0 | 441 | 0.2940 | 0.9224 | 0.9241 |
No log | 10.0 | 490 | 0.3138 | 0.9232 | 0.9250 |
0.132 | 11.0 | 539 | 0.3189 | 0.9256 | 0.9267 |
0.132 | 12.0 | 588 | 0.3139 | 0.9264 | 0.9272 |
0.132 | 13.0 | 637 | 0.3534 | 0.9203 | 0.9225 |
0.132 | 14.0 | 686 | 0.3330 | 0.9263 | 0.9260 |
0.132 | 15.0 | 735 | 0.3483 | 0.9242 | 0.9228 |
0.132 | 16.0 | 784 | 0.3483 | 0.9257 | 0.9261 |
0.132 | 17.0 | 833 | 0.3528 | 0.9261 | 0.9261 |
0.132 | 18.0 | 882 | 0.3479 | 0.9274 | 0.9276 |
0.132 | 19.0 | 931 | 0.3592 | 0.9246 | 0.9262 |
0.132 | 20.0 | 980 | 0.3537 | 0.9272 | 0.9270 |
0.0211 | 21.0 | 1029 | 0.3574 | 0.9271 | 0.9268 |
0.0211 | 22.0 | 1078 | 0.3615 | 0.9273 | 0.9281 |
0.0211 | 23.0 | 1127 | 0.3684 | 0.9281 | 0.9276 |
0.0211 | 24.0 | 1176 | 0.3753 | 0.9270 | 0.9281 |
0.0211 | 25.0 | 1225 | 0.3774 | 0.9278 | 0.9282 |
0.0211 | 26.0 | 1274 | 0.3893 | 0.9284 | 0.9289 |
0.0211 | 27.0 | 1323 | 0.3882 | 0.9282 | 0.9275 |
0.0211 | 28.0 | 1372 | 0.3900 | 0.927 | 0.9280 |
0.0211 | 29.0 | 1421 | 0.3910 | 0.9272 | 0.9282 |
0.0211 | 30.0 | 1470 | 0.3970 | 0.9279 | 0.9289 |
0.0112 | 31.0 | 1519 | 0.3985 | 0.9295 | 0.9300 |
0.0112 | 32.0 | 1568 | 0.4030 | 0.9288 | 0.9286 |
0.0112 | 33.0 | 1617 | 0.4075 | 0.9284 | 0.9283 |
0.0112 | 34.0 | 1666 | 0.4183 | 0.9273 | 0.9277 |
0.0112 | 35.0 | 1715 | 0.4235 | 0.9261 | 0.9269 |
0.0112 | 36.0 | 1764 | 0.4316 | 0.9272 | 0.9268 |
0.0112 | 37.0 | 1813 | 0.4231 | 0.9286 | 0.9285 |
0.0112 | 38.0 | 1862 | 0.4222 | 0.9289 | 0.9290 |
0.0112 | 39.0 | 1911 | 0.4256 | 0.9294 | 0.9290 |
0.0112 | 40.0 | 1960 | 0.4314 | 0.9288 | 0.9291 |
0.0053 | 41.0 | 2009 | 0.4291 | 0.9286 | 0.9288 |
0.0053 | 42.0 | 2058 | 0.4483 | 0.9266 | 0.9277 |
0.0053 | 43.0 | 2107 | 0.4392 | 0.9282 | 0.9287 |
0.0053 | 44.0 | 2156 | 0.4453 | 0.9282 | 0.9286 |
0.0053 | 45.0 | 2205 | 0.4562 | 0.9265 | 0.9276 |
0.0053 | 46.0 | 2254 | 0.4564 | 0.9264 | 0.9275 |
0.0053 | 47.0 | 2303 | 0.4471 | 0.9278 | 0.9281 |
0.0053 | 48.0 | 2352 | 0.4473 | 0.928 | 0.9282 |
0.0053 | 49.0 | 2401 | 0.4506 | 0.9281 | 0.9285 |
0.0053 | 50.0 | 2450 | 0.4498 | 0.9279 | 0.9283 |
Framework versions
- Transformers 4.33.2
- Pytorch 1.13.1+cu117
- Datasets 2.19.1
- Tokenizers 0.13.3