File size: 3,740 Bytes
13fea20 7012c4c 13fea20 7012c4c 13fea20 1e79fe2 13fea20 157e8b8 13fea20 157e8b8 13fea20 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
---
base_model: FacebookAI/xlm-roberta-base
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-non-kd-pre-ner-full-xlmr_data-univner_en44
results: []
---
<!-- 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. -->
# scenario-non-kd-pre-ner-full-xlmr_data-univner_en44
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1507
- Precision: 0.7345
- Recall: 0.7588
- F1: 0.7464
- Accuracy: 0.9803
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 44
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0869 | 1.2755 | 500 | 0.0715 | 0.6808 | 0.7308 | 0.7049 | 0.9773 |
| 0.0328 | 2.5510 | 1000 | 0.0781 | 0.7035 | 0.7319 | 0.7174 | 0.9784 |
| 0.0202 | 3.8265 | 1500 | 0.0805 | 0.7083 | 0.7340 | 0.7209 | 0.9791 |
| 0.0121 | 5.1020 | 2000 | 0.0914 | 0.7255 | 0.7495 | 0.7373 | 0.9792 |
| 0.0071 | 6.3776 | 2500 | 0.0988 | 0.7178 | 0.7557 | 0.7363 | 0.9787 |
| 0.005 | 7.6531 | 3000 | 0.1089 | 0.7178 | 0.7609 | 0.7387 | 0.9795 |
| 0.0033 | 8.9286 | 3500 | 0.1177 | 0.7353 | 0.7246 | 0.7299 | 0.9792 |
| 0.0033 | 10.2041 | 4000 | 0.1134 | 0.7219 | 0.7391 | 0.7304 | 0.9794 |
| 0.0022 | 11.4796 | 4500 | 0.1251 | 0.7243 | 0.7588 | 0.7412 | 0.9801 |
| 0.0023 | 12.7551 | 5000 | 0.1279 | 0.7070 | 0.7619 | 0.7334 | 0.9792 |
| 0.0017 | 14.0306 | 5500 | 0.1231 | 0.7165 | 0.7588 | 0.7371 | 0.9793 |
| 0.0014 | 15.3061 | 6000 | 0.1378 | 0.7289 | 0.7598 | 0.7440 | 0.9792 |
| 0.0014 | 16.5816 | 6500 | 0.1507 | 0.6986 | 0.7774 | 0.7359 | 0.9782 |
| 0.001 | 17.8571 | 7000 | 0.1421 | 0.7242 | 0.7474 | 0.7356 | 0.9793 |
| 0.0009 | 19.1327 | 7500 | 0.1396 | 0.7284 | 0.7578 | 0.7428 | 0.9796 |
| 0.0008 | 20.4082 | 8000 | 0.1394 | 0.7402 | 0.7226 | 0.7313 | 0.9788 |
| 0.0006 | 21.6837 | 8500 | 0.1425 | 0.7542 | 0.7371 | 0.7455 | 0.9797 |
| 0.0006 | 22.9592 | 9000 | 0.1446 | 0.7339 | 0.7308 | 0.7324 | 0.9793 |
| 0.0005 | 24.2347 | 9500 | 0.1459 | 0.7374 | 0.7557 | 0.7464 | 0.9804 |
| 0.0004 | 25.5102 | 10000 | 0.1443 | 0.7323 | 0.7505 | 0.7413 | 0.9800 |
| 0.0002 | 26.7857 | 10500 | 0.1485 | 0.7299 | 0.7526 | 0.7411 | 0.9801 |
| 0.0003 | 28.0612 | 11000 | 0.1507 | 0.7408 | 0.7484 | 0.7446 | 0.9803 |
| 0.0003 | 29.3367 | 11500 | 0.1507 | 0.7345 | 0.7588 | 0.7464 | 0.9803 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
|