update model card README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,96 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
tags:
|
| 4 |
+
- generated_from_trainer
|
| 5 |
+
metrics:
|
| 6 |
+
- precision
|
| 7 |
+
- recall
|
| 8 |
+
- f1
|
| 9 |
+
- accuracy
|
| 10 |
+
model-index:
|
| 11 |
+
- name: prueba
|
| 12 |
+
results: []
|
| 13 |
+
---
|
| 14 |
+
|
| 15 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 16 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 17 |
+
|
| 18 |
+
# prueba
|
| 19 |
+
|
| 20 |
+
This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer) on an unknown dataset.
|
| 21 |
+
It achieves the following results on the evaluation set:
|
| 22 |
+
- Loss: 0.1440
|
| 23 |
+
- Precision: 0.6923
|
| 24 |
+
- Recall: 0.6096
|
| 25 |
+
- F1: 0.6483
|
| 26 |
+
- Accuracy: 0.9719
|
| 27 |
+
|
| 28 |
+
## Model description
|
| 29 |
+
|
| 30 |
+
More information needed
|
| 31 |
+
|
| 32 |
+
## Intended uses & limitations
|
| 33 |
+
|
| 34 |
+
More information needed
|
| 35 |
+
|
| 36 |
+
## Training and evaluation data
|
| 37 |
+
|
| 38 |
+
More information needed
|
| 39 |
+
|
| 40 |
+
## Training procedure
|
| 41 |
+
|
| 42 |
+
### Training hyperparameters
|
| 43 |
+
|
| 44 |
+
The following hyperparameters were used during training:
|
| 45 |
+
- learning_rate: 2.5e-05
|
| 46 |
+
- train_batch_size: 32
|
| 47 |
+
- eval_batch_size: 8
|
| 48 |
+
- seed: 42
|
| 49 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 50 |
+
- lr_scheduler_type: linear
|
| 51 |
+
- num_epochs: 32
|
| 52 |
+
|
| 53 |
+
### Training results
|
| 54 |
+
|
| 55 |
+
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|
| 56 |
+
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
|
| 57 |
+
| No log | 1.0 | 29 | 0.3513 | 0.0 | 0.0 | 0.0 | 0.9259 |
|
| 58 |
+
| No log | 2.0 | 58 | 0.2696 | 0.0 | 0.0 | 0.0 | 0.9259 |
|
| 59 |
+
| No log | 3.0 | 87 | 0.2879 | 0.0 | 0.0 | 0.0 | 0.9259 |
|
| 60 |
+
| No log | 4.0 | 116 | 0.2318 | 0.0714 | 0.0080 | 0.0143 | 0.9361 |
|
| 61 |
+
| No log | 5.0 | 145 | 0.2055 | 0.2222 | 0.0558 | 0.0892 | 0.9376 |
|
| 62 |
+
| No log | 6.0 | 174 | 0.2076 | 0.3793 | 0.0876 | 0.1424 | 0.9464 |
|
| 63 |
+
| No log | 7.0 | 203 | 0.1630 | 0.4831 | 0.2271 | 0.3089 | 0.9525 |
|
| 64 |
+
| No log | 8.0 | 232 | 0.1529 | 0.5515 | 0.3625 | 0.4375 | 0.9573 |
|
| 65 |
+
| No log | 9.0 | 261 | 0.1519 | 0.5972 | 0.3426 | 0.4354 | 0.9603 |
|
| 66 |
+
| No log | 10.0 | 290 | 0.1399 | 0.6272 | 0.4223 | 0.5048 | 0.9639 |
|
| 67 |
+
| No log | 11.0 | 319 | 0.1412 | 0.6096 | 0.4542 | 0.5205 | 0.9641 |
|
| 68 |
+
| No log | 12.0 | 348 | 0.1320 | 0.5969 | 0.4661 | 0.5235 | 0.9646 |
|
| 69 |
+
| No log | 13.0 | 377 | 0.1311 | 0.6515 | 0.5139 | 0.5746 | 0.9671 |
|
| 70 |
+
| No log | 14.0 | 406 | 0.1300 | 0.6329 | 0.5219 | 0.5721 | 0.9656 |
|
| 71 |
+
| No log | 15.0 | 435 | 0.1346 | 0.6345 | 0.4980 | 0.5580 | 0.9672 |
|
| 72 |
+
| No log | 16.0 | 464 | 0.1361 | 0.6329 | 0.5219 | 0.5721 | 0.9669 |
|
| 73 |
+
| No log | 17.0 | 493 | 0.1312 | 0.6532 | 0.5777 | 0.6131 | 0.9689 |
|
| 74 |
+
| 0.1181 | 18.0 | 522 | 0.1327 | 0.6756 | 0.6056 | 0.6387 | 0.9694 |
|
| 75 |
+
| 0.1181 | 19.0 | 551 | 0.1495 | 0.7234 | 0.5418 | 0.6196 | 0.9704 |
|
| 76 |
+
| 0.1181 | 20.0 | 580 | 0.1328 | 0.6872 | 0.5777 | 0.6277 | 0.9707 |
|
| 77 |
+
| 0.1181 | 21.0 | 609 | 0.1363 | 0.6667 | 0.6215 | 0.6433 | 0.9710 |
|
| 78 |
+
| 0.1181 | 22.0 | 638 | 0.1392 | 0.6884 | 0.5896 | 0.6352 | 0.9712 |
|
| 79 |
+
| 0.1181 | 23.0 | 667 | 0.1377 | 0.6437 | 0.6335 | 0.6386 | 0.9704 |
|
| 80 |
+
| 0.1181 | 24.0 | 696 | 0.1434 | 0.6504 | 0.5857 | 0.6164 | 0.9697 |
|
| 81 |
+
| 0.1181 | 25.0 | 725 | 0.1418 | 0.6944 | 0.5976 | 0.6424 | 0.9710 |
|
| 82 |
+
| 0.1181 | 26.0 | 754 | 0.1426 | 0.6739 | 0.6175 | 0.6445 | 0.9715 |
|
| 83 |
+
| 0.1181 | 27.0 | 783 | 0.1447 | 0.7085 | 0.6295 | 0.6667 | 0.9734 |
|
| 84 |
+
| 0.1181 | 28.0 | 812 | 0.1432 | 0.6903 | 0.6215 | 0.6541 | 0.9727 |
|
| 85 |
+
| 0.1181 | 29.0 | 841 | 0.1421 | 0.7162 | 0.6335 | 0.6723 | 0.9729 |
|
| 86 |
+
| 0.1181 | 30.0 | 870 | 0.1431 | 0.6875 | 0.6135 | 0.6484 | 0.9720 |
|
| 87 |
+
| 0.1181 | 31.0 | 899 | 0.1431 | 0.6844 | 0.6135 | 0.6471 | 0.9717 |
|
| 88 |
+
| 0.1181 | 32.0 | 928 | 0.1440 | 0.6923 | 0.6096 | 0.6483 | 0.9719 |
|
| 89 |
+
|
| 90 |
+
|
| 91 |
+
### Framework versions
|
| 92 |
+
|
| 93 |
+
- Transformers 4.27.3
|
| 94 |
+
- Pytorch 1.13.1+cu116
|
| 95 |
+
- Datasets 2.10.1
|
| 96 |
+
- Tokenizers 0.13.2
|