Initial Commit
Browse files- README.md +169 -0
- config.json +46 -0
- eval_result_ner.json +1 -0
- model.safetensors +3 -0
- training_args.bin +3 -0
README.md
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| 1 |
+
---
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| 2 |
+
library_name: transformers
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| 3 |
+
license: mit
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+
base_model: FacebookAI/xlm-roberta-base
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+
tags:
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| 6 |
+
- generated_from_trainer
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| 7 |
+
metrics:
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| 8 |
+
- precision
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| 9 |
+
- recall
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| 10 |
+
- f1
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| 11 |
+
- accuracy
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| 12 |
+
model-index:
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| 13 |
+
- name: scenario-non-kd-scr-ner-half-xlmr_data-univner_full66
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| 14 |
+
results: []
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| 15 |
+
---
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| 16 |
+
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| 17 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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| 18 |
+
should probably proofread and complete it, then remove this comment. -->
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| 19 |
+
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| 20 |
+
# scenario-non-kd-scr-ner-half-xlmr_data-univner_full66
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| 21 |
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+
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
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| 23 |
+
It achieves the following results on the evaluation set:
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| 24 |
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- Loss: 0.3523
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| 25 |
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- Precision: 0.5419
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- Recall: 0.5460
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| 27 |
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- F1: 0.5439
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| 28 |
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- Accuracy: 0.9571
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| 29 |
+
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| 30 |
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## Model description
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| 31 |
+
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| 32 |
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More information needed
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| 33 |
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| 34 |
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## Intended uses & limitations
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| 35 |
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| 36 |
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More information needed
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| 37 |
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| 38 |
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## Training and evaluation data
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| 39 |
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| 40 |
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More information needed
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| 41 |
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| 42 |
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## Training procedure
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| 43 |
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| 44 |
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### Training hyperparameters
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| 45 |
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| 46 |
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The following hyperparameters were used during training:
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| 47 |
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- learning_rate: 3e-05
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- train_batch_size: 32
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| 49 |
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- eval_batch_size: 32
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- seed: 66
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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| 52 |
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- lr_scheduler_type: linear
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| 53 |
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- num_epochs: 30
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| 54 |
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| 55 |
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### Training results
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| 56 |
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| 57 |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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| 58 |
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|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 59 |
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| 0.3585 | 0.2910 | 500 | 0.2974 | 0.4893 | 0.0856 | 0.1456 | 0.9279 |
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| 60 |
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| 0.2746 | 0.5821 | 1000 | 0.2792 | 0.4135 | 0.1262 | 0.1934 | 0.9310 |
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| 61 |
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| 0.2447 | 0.8731 | 1500 | 0.2385 | 0.3118 | 0.2189 | 0.2572 | 0.9345 |
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| 62 |
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| 0.2219 | 1.1641 | 2000 | 0.2317 | 0.3320 | 0.2402 | 0.2788 | 0.9370 |
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| 63 |
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| 0.2027 | 1.4552 | 2500 | 0.2292 | 0.3533 | 0.2369 | 0.2836 | 0.9382 |
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| 64 |
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| 0.1957 | 1.7462 | 3000 | 0.2157 | 0.3664 | 0.2812 | 0.3182 | 0.9398 |
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| 65 |
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| 0.1835 | 2.0373 | 3500 | 0.2089 | 0.3606 | 0.3528 | 0.3566 | 0.9398 |
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| 66 |
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| 0.1641 | 2.3283 | 4000 | 0.2153 | 0.3493 | 0.3210 | 0.3346 | 0.9382 |
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| 67 |
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| 0.1642 | 2.6193 | 4500 | 0.2119 | 0.3507 | 0.3284 | 0.3392 | 0.9398 |
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| 68 |
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| 0.1638 | 2.9104 | 5000 | 0.1977 | 0.3630 | 0.3696 | 0.3663 | 0.9419 |
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| 69 |
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| 0.144 | 3.2014 | 5500 | 0.2035 | 0.3921 | 0.3922 | 0.3921 | 0.9420 |
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| 70 |
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| 0.1408 | 3.4924 | 6000 | 0.2045 | 0.4286 | 0.3652 | 0.3944 | 0.9440 |
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| 71 |
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| 0.1362 | 3.7835 | 6500 | 0.2080 | 0.4012 | 0.3520 | 0.3750 | 0.9439 |
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| 72 |
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| 0.1359 | 4.0745 | 7000 | 0.2061 | 0.4102 | 0.3949 | 0.4024 | 0.9437 |
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| 73 |
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| 0.1199 | 4.3655 | 7500 | 0.2102 | 0.4082 | 0.3854 | 0.3964 | 0.9445 |
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| 74 |
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| 0.1226 | 4.6566 | 8000 | 0.2082 | 0.4303 | 0.3819 | 0.4047 | 0.9452 |
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| 75 |
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| 0.1217 | 4.9476 | 8500 | 0.2006 | 0.4307 | 0.3947 | 0.4120 | 0.9463 |
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| 76 |
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| 0.1079 | 5.2386 | 9000 | 0.2079 | 0.4370 | 0.4158 | 0.4261 | 0.9468 |
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| 77 |
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| 0.1037 | 5.5297 | 9500 | 0.2046 | 0.4334 | 0.4171 | 0.4251 | 0.9472 |
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| 78 |
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| 0.0998 | 5.8207 | 10000 | 0.1919 | 0.4363 | 0.4564 | 0.4461 | 0.9466 |
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| 79 |
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| 0.0938 | 6.1118 | 10500 | 0.2085 | 0.4477 | 0.4333 | 0.4404 | 0.9477 |
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| 80 |
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| 0.0858 | 6.4028 | 11000 | 0.2107 | 0.4499 | 0.4577 | 0.4537 | 0.9487 |
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| 81 |
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| 0.0827 | 6.6938 | 11500 | 0.2065 | 0.4394 | 0.4810 | 0.4593 | 0.9484 |
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| 82 |
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| 0.0845 | 6.9849 | 12000 | 0.2059 | 0.4668 | 0.4497 | 0.4581 | 0.9500 |
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| 83 |
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| 0.0698 | 7.2759 | 12500 | 0.2204 | 0.4713 | 0.4546 | 0.4628 | 0.9506 |
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| 84 |
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| 0.0677 | 7.5669 | 13000 | 0.2104 | 0.4597 | 0.4779 | 0.4686 | 0.9499 |
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| 85 |
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| 0.0687 | 7.8580 | 13500 | 0.2106 | 0.4765 | 0.4858 | 0.4811 | 0.9515 |
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| 86 |
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| 0.0628 | 8.1490 | 14000 | 0.2160 | 0.4793 | 0.4882 | 0.4837 | 0.9524 |
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| 87 |
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| 0.0543 | 8.4400 | 14500 | 0.2129 | 0.4710 | 0.5076 | 0.4886 | 0.9509 |
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| 88 |
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| 0.0551 | 8.7311 | 15000 | 0.2190 | 0.4965 | 0.4874 | 0.4919 | 0.9525 |
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| 89 |
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| 0.0556 | 9.0221 | 15500 | 0.2149 | 0.5022 | 0.4999 | 0.5011 | 0.9532 |
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| 90 |
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| 0.0455 | 9.3132 | 16000 | 0.2233 | 0.5080 | 0.5126 | 0.5103 | 0.9537 |
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| 91 |
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| 0.0453 | 9.6042 | 16500 | 0.2235 | 0.4936 | 0.5167 | 0.5049 | 0.9532 |
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| 92 |
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| 0.0459 | 9.8952 | 17000 | 0.2256 | 0.5156 | 0.5044 | 0.5100 | 0.9535 |
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| 93 |
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| 0.0403 | 10.1863 | 17500 | 0.2380 | 0.5120 | 0.5090 | 0.5105 | 0.9542 |
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| 94 |
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| 0.0378 | 10.4773 | 18000 | 0.2332 | 0.4895 | 0.5243 | 0.5063 | 0.9532 |
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| 95 |
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| 0.0373 | 10.7683 | 18500 | 0.2402 | 0.5136 | 0.5139 | 0.5138 | 0.9542 |
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| 96 |
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| 0.0383 | 11.0594 | 19000 | 0.2437 | 0.5165 | 0.5092 | 0.5128 | 0.9546 |
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| 97 |
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| 0.033 | 11.3504 | 19500 | 0.2444 | 0.4994 | 0.5305 | 0.5145 | 0.9540 |
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| 98 |
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| 0.0324 | 11.6414 | 20000 | 0.2489 | 0.5093 | 0.5194 | 0.5143 | 0.9542 |
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| 99 |
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| 0.0313 | 11.9325 | 20500 | 0.2462 | 0.4995 | 0.5354 | 0.5168 | 0.9542 |
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| 100 |
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| 0.0283 | 12.2235 | 21000 | 0.2513 | 0.5042 | 0.5340 | 0.5187 | 0.9540 |
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| 101 |
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| 0.0274 | 12.5146 | 21500 | 0.2575 | 0.5023 | 0.5356 | 0.5184 | 0.9539 |
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| 102 |
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| 0.0271 | 12.8056 | 22000 | 0.2670 | 0.5201 | 0.5210 | 0.5205 | 0.9551 |
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| 103 |
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| 0.026 | 13.0966 | 22500 | 0.2643 | 0.5095 | 0.5198 | 0.5146 | 0.9543 |
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| 104 |
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| 0.0231 | 13.3877 | 23000 | 0.2650 | 0.5129 | 0.5348 | 0.5237 | 0.9546 |
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| 105 |
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| 0.0241 | 13.6787 | 23500 | 0.2675 | 0.5305 | 0.5308 | 0.5307 | 0.9554 |
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| 106 |
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| 0.0238 | 13.9697 | 24000 | 0.2718 | 0.5249 | 0.5139 | 0.5194 | 0.9552 |
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| 107 |
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| 0.0213 | 14.2608 | 24500 | 0.2713 | 0.5141 | 0.5273 | 0.5206 | 0.9547 |
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| 108 |
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| 0.0197 | 14.5518 | 25000 | 0.2726 | 0.5235 | 0.5304 | 0.5269 | 0.9557 |
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| 109 |
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| 0.0205 | 14.8428 | 25500 | 0.2745 | 0.5229 | 0.5359 | 0.5293 | 0.9558 |
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| 110 |
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| 0.0193 | 15.1339 | 26000 | 0.2829 | 0.5130 | 0.5347 | 0.5236 | 0.9552 |
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| 111 |
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| 0.0177 | 15.4249 | 26500 | 0.2794 | 0.5175 | 0.5370 | 0.5271 | 0.9555 |
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| 112 |
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| 0.0175 | 15.7159 | 27000 | 0.2831 | 0.5369 | 0.5275 | 0.5321 | 0.9559 |
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| 113 |
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| 0.0166 | 16.0070 | 27500 | 0.2869 | 0.5280 | 0.5276 | 0.5278 | 0.9556 |
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| 114 |
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| 0.0143 | 16.2980 | 28000 | 0.2919 | 0.5234 | 0.5363 | 0.5298 | 0.9561 |
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| 115 |
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| 0.0155 | 16.5891 | 28500 | 0.2957 | 0.5242 | 0.5392 | 0.5316 | 0.9558 |
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| 116 |
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| 0.0159 | 16.8801 | 29000 | 0.2924 | 0.5148 | 0.5561 | 0.5346 | 0.9556 |
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| 117 |
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| 0.0132 | 17.1711 | 29500 | 0.2929 | 0.5217 | 0.5490 | 0.5350 | 0.9559 |
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| 118 |
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| 0.0141 | 17.4622 | 30000 | 0.3024 | 0.5302 | 0.5340 | 0.5321 | 0.9563 |
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| 119 |
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| 0.0136 | 17.7532 | 30500 | 0.3012 | 0.5339 | 0.5314 | 0.5326 | 0.9563 |
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| 120 |
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| 0.0128 | 18.0442 | 31000 | 0.3084 | 0.5364 | 0.5343 | 0.5353 | 0.9565 |
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| 121 |
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| 0.0117 | 18.3353 | 31500 | 0.3098 | 0.5451 | 0.5219 | 0.5332 | 0.9562 |
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| 122 |
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| 0.0115 | 18.6263 | 32000 | 0.3144 | 0.5454 | 0.5214 | 0.5332 | 0.9563 |
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| 123 |
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| 0.0121 | 18.9173 | 32500 | 0.3112 | 0.5403 | 0.5324 | 0.5363 | 0.9565 |
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| 124 |
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| 0.01 | 19.2084 | 33000 | 0.3180 | 0.5348 | 0.5400 | 0.5374 | 0.9564 |
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| 125 |
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| 0.0111 | 19.4994 | 33500 | 0.3123 | 0.5348 | 0.5395 | 0.5371 | 0.9565 |
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| 126 |
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| 0.0105 | 19.7905 | 34000 | 0.3103 | 0.5309 | 0.5458 | 0.5382 | 0.9562 |
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| 127 |
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| 0.0101 | 20.0815 | 34500 | 0.3217 | 0.5326 | 0.5302 | 0.5314 | 0.9561 |
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| 128 |
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| 0.0095 | 20.3725 | 35000 | 0.3184 | 0.5190 | 0.5510 | 0.5345 | 0.9561 |
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| 129 |
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| 0.0093 | 20.6636 | 35500 | 0.3201 | 0.5386 | 0.5514 | 0.5449 | 0.9566 |
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| 130 |
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| 0.0093 | 20.9546 | 36000 | 0.3246 | 0.5428 | 0.5464 | 0.5446 | 0.9569 |
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| 131 |
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| 0.0085 | 21.2456 | 36500 | 0.3209 | 0.5421 | 0.5523 | 0.5471 | 0.9567 |
|
| 132 |
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| 0.0078 | 21.5367 | 37000 | 0.3293 | 0.5353 | 0.5356 | 0.5354 | 0.9567 |
|
| 133 |
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| 0.0091 | 21.8277 | 37500 | 0.3241 | 0.5348 | 0.5470 | 0.5408 | 0.9564 |
|
| 134 |
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| 0.0077 | 22.1187 | 38000 | 0.3336 | 0.5410 | 0.5434 | 0.5422 | 0.9568 |
|
| 135 |
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| 0.0064 | 22.4098 | 38500 | 0.3349 | 0.5376 | 0.5412 | 0.5394 | 0.9566 |
|
| 136 |
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| 0.0079 | 22.7008 | 39000 | 0.3343 | 0.5329 | 0.5473 | 0.5400 | 0.9566 |
|
| 137 |
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| 0.0081 | 22.9919 | 39500 | 0.3335 | 0.5377 | 0.5493 | 0.5434 | 0.9567 |
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| 138 |
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| 0.0071 | 23.2829 | 40000 | 0.3370 | 0.5427 | 0.5442 | 0.5435 | 0.9569 |
|
| 139 |
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| 0.0062 | 23.5739 | 40500 | 0.3350 | 0.5296 | 0.5537 | 0.5414 | 0.9566 |
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| 140 |
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| 0.0069 | 23.8650 | 41000 | 0.3383 | 0.5400 | 0.5399 | 0.5399 | 0.9569 |
|
| 141 |
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| 0.0071 | 24.1560 | 41500 | 0.3420 | 0.5356 | 0.5387 | 0.5372 | 0.9567 |
|
| 142 |
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| 0.0057 | 24.4470 | 42000 | 0.3418 | 0.5350 | 0.5439 | 0.5394 | 0.9567 |
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| 143 |
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| 0.0065 | 24.7381 | 42500 | 0.3442 | 0.5375 | 0.5434 | 0.5404 | 0.9568 |
|
| 144 |
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| 0.0063 | 25.0291 | 43000 | 0.3467 | 0.5441 | 0.5373 | 0.5407 | 0.9568 |
|
| 145 |
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| 0.0051 | 25.3201 | 43500 | 0.3440 | 0.5312 | 0.5511 | 0.5410 | 0.9566 |
|
| 146 |
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| 0.0063 | 25.6112 | 44000 | 0.3452 | 0.5333 | 0.5475 | 0.5403 | 0.9569 |
|
| 147 |
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| 0.0062 | 25.9022 | 44500 | 0.3490 | 0.5405 | 0.5432 | 0.5418 | 0.9568 |
|
| 148 |
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| 0.005 | 26.1932 | 45000 | 0.3465 | 0.5365 | 0.5467 | 0.5416 | 0.9568 |
|
| 149 |
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| 0.0054 | 26.4843 | 45500 | 0.3477 | 0.5437 | 0.5425 | 0.5431 | 0.9569 |
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| 150 |
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| 0.0046 | 26.7753 | 46000 | 0.3462 | 0.5413 | 0.5431 | 0.5422 | 0.9568 |
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| 151 |
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| 0.006 | 27.0664 | 46500 | 0.3479 | 0.5466 | 0.5530 | 0.5498 | 0.9574 |
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| 152 |
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| 0.0049 | 27.3574 | 47000 | 0.3515 | 0.5413 | 0.5434 | 0.5423 | 0.9569 |
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| 153 |
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| 0.0046 | 27.6484 | 47500 | 0.3507 | 0.5399 | 0.5448 | 0.5423 | 0.9570 |
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| 154 |
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| 0.0053 | 27.9395 | 48000 | 0.3497 | 0.541 | 0.5464 | 0.5437 | 0.9569 |
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| 155 |
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| 0.0049 | 28.2305 | 48500 | 0.3527 | 0.5354 | 0.5480 | 0.5416 | 0.9568 |
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| 156 |
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| 0.0048 | 28.5215 | 49000 | 0.3554 | 0.5449 | 0.5442 | 0.5445 | 0.9572 |
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| 157 |
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| 0.0049 | 28.8126 | 49500 | 0.3532 | 0.5413 | 0.5473 | 0.5443 | 0.9571 |
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| 158 |
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| 0.0041 | 29.1036 | 50000 | 0.3531 | 0.54 | 0.5454 | 0.5427 | 0.9569 |
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| 159 |
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| 0.0043 | 29.3946 | 50500 | 0.3524 | 0.5414 | 0.5483 | 0.5448 | 0.9571 |
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| 160 |
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| 0.0048 | 29.6857 | 51000 | 0.3519 | 0.5409 | 0.5480 | 0.5444 | 0.9571 |
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| 161 |
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| 0.0041 | 29.9767 | 51500 | 0.3523 | 0.5419 | 0.5460 | 0.5439 | 0.9571 |
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| 162 |
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| 163 |
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| 164 |
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### Framework versions
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| 165 |
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| 166 |
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- Transformers 4.44.2
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| 167 |
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- Pytorch 2.1.1+cu121
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| 168 |
+
- Datasets 2.14.5
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| 169 |
+
- Tokenizers 0.19.1
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config.json
ADDED
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| 1 |
+
{
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| 2 |
+
"_name_or_path": "FacebookAI/xlm-roberta-base",
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| 3 |
+
"architectures": [
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| 4 |
+
"XLMRobertaForTokenClassification"
|
| 5 |
+
],
|
| 6 |
+
"attention_probs_dropout_prob": 0.1,
|
| 7 |
+
"bos_token_id": 0,
|
| 8 |
+
"classifier_dropout": null,
|
| 9 |
+
"eos_token_id": 2,
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| 10 |
+
"hidden_act": "gelu",
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| 11 |
+
"hidden_dropout_prob": 0.1,
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| 12 |
+
"hidden_size": 384,
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| 13 |
+
"id2label": {
|
| 14 |
+
"0": "LABEL_0",
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| 15 |
+
"1": "LABEL_1",
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| 16 |
+
"2": "LABEL_2",
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| 17 |
+
"3": "LABEL_3",
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| 18 |
+
"4": "LABEL_4",
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| 19 |
+
"5": "LABEL_5",
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| 20 |
+
"6": "LABEL_6"
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| 21 |
+
},
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| 22 |
+
"initializer_range": 0.02,
|
| 23 |
+
"intermediate_size": 1536,
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| 24 |
+
"label2id": {
|
| 25 |
+
"LABEL_0": 0,
|
| 26 |
+
"LABEL_1": 1,
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| 27 |
+
"LABEL_2": 2,
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| 28 |
+
"LABEL_3": 3,
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| 29 |
+
"LABEL_4": 4,
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| 30 |
+
"LABEL_5": 5,
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| 31 |
+
"LABEL_6": 6
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| 32 |
+
},
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| 33 |
+
"layer_norm_eps": 1e-05,
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| 34 |
+
"max_position_embeddings": 514,
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| 35 |
+
"model_type": "xlm-roberta",
|
| 36 |
+
"num_attention_heads": 12,
|
| 37 |
+
"num_hidden_layers": 6,
|
| 38 |
+
"output_past": true,
|
| 39 |
+
"pad_token_id": 1,
|
| 40 |
+
"position_embedding_type": "absolute",
|
| 41 |
+
"torch_dtype": "float32",
|
| 42 |
+
"transformers_version": "4.44.2",
|
| 43 |
+
"type_vocab_size": 1,
|
| 44 |
+
"use_cache": true,
|
| 45 |
+
"vocab_size": 250002
|
| 46 |
+
}
|
eval_result_ner.json
ADDED
|
@@ -0,0 +1 @@
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|
| 1 |
+
{"ceb_gja": {"precision": 0.32323232323232326, "recall": 0.6530612244897959, "f1": 0.43243243243243246, "accuracy": 0.9312741312741313}, "en_pud": {"precision": 0.4124748490945674, "recall": 0.3813953488372093, "f1": 0.39632672788786855, "accuracy": 0.9450793350963355}, "de_pud": {"precision": 0.09352078239608802, "recall": 0.2945139557266602, "f1": 0.1419624217118998, "accuracy": 0.805119309924523}, "pt_pud": {"precision": 0.4790575916230366, "recall": 0.49954504094631486, "f1": 0.48908685968819593, "accuracy": 0.9556115691887043}, "ru_pud": {"precision": 0.011983736357800128, "recall": 0.05405405405405406, "f1": 0.019618146785776842, "accuracy": 0.5726685610953242}, "sv_pud": {"precision": 0.4809052333804809, "recall": 0.3304178814382896, "f1": 0.39170506912442393, "accuracy": 0.9441706856783393}, "tl_trg": {"precision": 0.22580645161290322, "recall": 0.6086956521739131, "f1": 0.3294117647058823, "accuracy": 0.9237057220708447}, "tl_ugnayan": {"precision": 0.02197802197802198, "recall": 0.06060606060606061, "f1": 0.03225806451612903, "accuracy": 0.8851412944393802}, "zh_gsd": {"precision": 0.41656050955414015, "recall": 0.42633637548891784, "f1": 0.42139175257731953, "accuracy": 0.9234931734931735}, "zh_gsdsimp": {"precision": 0.41719342604298354, "recall": 0.4325032765399738, "f1": 0.4247104247104247, "accuracy": 0.9239094239094239}, "hr_set": {"precision": 0.6563965170797053, "recall": 0.6985032074126871, "f1": 0.6767955801104972, "accuracy": 0.9643033800494641}, "da_ddt": {"precision": 0.5943396226415094, "recall": 0.5637583892617449, "f1": 0.5786452353616534, "accuracy": 0.9697695300808141}, "en_ewt": {"precision": 0.5303482587064676, "recall": 0.4898897058823529, "f1": 0.5093167701863354, "accuracy": 0.9548551619715504}, "pt_bosque": {"precision": 0.525828835774865, "recall": 0.5613168724279836, "f1": 0.5429936305732485, "accuracy": 0.9609839153745834}, "sr_set": {"precision": 0.7141242937853107, "recall": 0.7461629279811098, "f1": 0.7297921478060047, "accuracy": 0.9617371508624464}, "sk_snk": {"precision": 0.33706293706293705, "recall": 0.2633879781420765, "f1": 0.29570552147239265, "accuracy": 0.9119032663316583}, "sv_talbanken": {"precision": 0.6707317073170732, "recall": 0.5612244897959183, "f1": 0.611111111111111, "accuracy": 0.9932276586347353}}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
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|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d6abce19f26a11d467695254ddcf592baf457fa50d89f4999f67cac6d6354294
|
| 3 |
+
size 427407404
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
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|
|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e34454730948d8f9a334cdab1dd2e5ff51ca74c9023c3a7407477c208fa18c32
|
| 3 |
+
size 5304
|