File size: 3,236 Bytes
74ee00a aa377d2 74ee00a aa377d2 74ee00a |
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 |
---
base_model: haryoaw/scenario-TCR-NER_data-univner_half
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-kd-scr-ner-full-xlmr_data-univner_half66
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-kd-scr-ner-full-xlmr_data-univner_half66
This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_half](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_half) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 239.7216
- Precision: 0.3760
- Recall: 0.2925
- F1: 0.3290
- Accuracy: 0.9383
## 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: 8
- eval_batch_size: 32
- seed: 66
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 445.7648 | 0.5828 | 500 | 369.2686 | 0.0 | 0.0 | 0.0 | 0.9241 |
| 345.7662 | 1.1655 | 1000 | 344.5894 | 0.3114 | 0.0688 | 0.1127 | 0.9255 |
| 317.4014 | 1.7483 | 1500 | 321.3392 | 0.3477 | 0.0814 | 0.1319 | 0.9268 |
| 296.2663 | 2.3310 | 2000 | 304.3693 | 0.4262 | 0.0837 | 0.1399 | 0.9278 |
| 278.6608 | 2.9138 | 2500 | 293.9949 | 0.3311 | 0.1453 | 0.2020 | 0.9306 |
| 265.0553 | 3.4965 | 3000 | 282.5093 | 0.3451 | 0.1672 | 0.2253 | 0.9328 |
| 253.4131 | 4.0793 | 3500 | 275.8793 | 0.3284 | 0.2137 | 0.2589 | 0.9337 |
| 243.5783 | 4.6620 | 4000 | 268.4692 | 0.3237 | 0.2345 | 0.2719 | 0.9341 |
| 235.5956 | 5.2448 | 4500 | 262.3924 | 0.3467 | 0.2506 | 0.2909 | 0.9355 |
| 228.406 | 5.8275 | 5000 | 257.0986 | 0.3634 | 0.2381 | 0.2877 | 0.9361 |
| 222.6923 | 6.4103 | 5500 | 250.0558 | 0.3799 | 0.2350 | 0.2904 | 0.9380 |
| 218.056 | 6.9930 | 6000 | 246.7546 | 0.3904 | 0.2479 | 0.3032 | 0.9383 |
| 213.6749 | 7.5758 | 6500 | 245.7390 | 0.3713 | 0.2721 | 0.3141 | 0.9373 |
| 210.549 | 8.1585 | 7000 | 242.9818 | 0.3644 | 0.2611 | 0.3043 | 0.9367 |
| 207.9283 | 8.7413 | 7500 | 240.6766 | 0.3721 | 0.2789 | 0.3188 | 0.9376 |
| 206.1746 | 9.3240 | 8000 | 239.1118 | 0.3990 | 0.2772 | 0.3271 | 0.9391 |
| 205.302 | 9.9068 | 8500 | 239.7216 | 0.3760 | 0.2925 | 0.3290 | 0.9383 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
|