File size: 3,777 Bytes
c5e405e |
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 |
---
library_name: transformers
license: mit
base_model: haryoaw/scenario-TCR-NER_data-univner_en
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scenario-non-kd-po-ner-full-xlmr_data-univner_en66
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-po-ner-full-xlmr_data-univner_en66
This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_en](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1477
- Precision: 0.8101
- Recall: 0.7992
- F1: 0.8046
- Accuracy: 0.9836
## 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: 66
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0036 | 1.2755 | 500 | 0.1194 | 0.8112 | 0.7785 | 0.7945 | 0.9831 |
| 0.0035 | 2.5510 | 1000 | 0.1172 | 0.7899 | 0.8137 | 0.8016 | 0.9838 |
| 0.0029 | 3.8265 | 1500 | 0.1173 | 0.7804 | 0.7909 | 0.7856 | 0.9822 |
| 0.0019 | 5.1020 | 2000 | 0.1175 | 0.7907 | 0.7940 | 0.7924 | 0.9841 |
| 0.0018 | 6.3776 | 2500 | 0.1271 | 0.7935 | 0.7836 | 0.7885 | 0.9830 |
| 0.0021 | 7.6531 | 3000 | 0.1281 | 0.7975 | 0.7909 | 0.7942 | 0.9837 |
| 0.0012 | 8.9286 | 3500 | 0.1170 | 0.7908 | 0.8023 | 0.7965 | 0.9843 |
| 0.0018 | 10.2041 | 4000 | 0.1324 | 0.8084 | 0.7950 | 0.8017 | 0.9840 |
| 0.0011 | 11.4796 | 4500 | 0.1304 | 0.7926 | 0.8188 | 0.8055 | 0.9839 |
| 0.0007 | 12.7551 | 5000 | 0.1370 | 0.7958 | 0.7867 | 0.7913 | 0.9838 |
| 0.0011 | 14.0306 | 5500 | 0.1297 | 0.7867 | 0.8095 | 0.7980 | 0.9837 |
| 0.0012 | 15.3061 | 6000 | 0.1254 | 0.7772 | 0.8126 | 0.7945 | 0.9830 |
| 0.0007 | 16.5816 | 6500 | 0.1374 | 0.8304 | 0.7909 | 0.8102 | 0.9831 |
| 0.0006 | 17.8571 | 7000 | 0.1369 | 0.7903 | 0.8116 | 0.8008 | 0.9832 |
| 0.0003 | 19.1327 | 7500 | 0.1379 | 0.7961 | 0.8043 | 0.8002 | 0.9841 |
| 0.0003 | 20.4082 | 8000 | 0.1365 | 0.7953 | 0.8002 | 0.7977 | 0.9838 |
| 0.0004 | 21.6837 | 8500 | 0.1458 | 0.7879 | 0.8230 | 0.8051 | 0.9835 |
| 0.0004 | 22.9592 | 9000 | 0.1475 | 0.8101 | 0.7992 | 0.8046 | 0.9835 |
| 0.0004 | 24.2347 | 9500 | 0.1405 | 0.7931 | 0.8054 | 0.7992 | 0.9837 |
| 0.0001 | 25.5102 | 10000 | 0.1391 | 0.7949 | 0.8147 | 0.8047 | 0.9843 |
| 0.0002 | 26.7857 | 10500 | 0.1432 | 0.8067 | 0.8033 | 0.8050 | 0.9840 |
| 0.0002 | 28.0612 | 11000 | 0.1439 | 0.8067 | 0.7992 | 0.8029 | 0.9836 |
| 0.0001 | 29.3367 | 11500 | 0.1477 | 0.8101 | 0.7992 | 0.8046 | 0.9836 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
|