File size: 3,898 Bytes
47fc0f6 |
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 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
---
license: apache-2.0
base_model: dslim/distilbert-NER
tags:
- generated_from_trainer
datasets:
- transformer_dataset_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: huner_ncbi_disease_dslim
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: transformer_dataset_ner
type: transformer_dataset_ner
config: ncbi_disease
split: validation
args: ncbi_disease
metrics:
- name: Precision
type: precision
value: 0.8325183374083129
- name: Recall
type: recall
value: 0.8653113087674714
- name: F1
type: f1
value: 0.8485981308411215
- name: Accuracy
type: accuracy
value: 0.9849891909996041
---
<!-- 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. -->
# huner_ncbi_disease_dslim
This model is a fine-tuned version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) on the transformer_dataset_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1484
- Precision: 0.8325
- Recall: 0.8653
- F1: 0.8486
- Accuracy: 0.9850
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1243 | 1.0 | 667 | 0.0669 | 0.7013 | 0.8412 | 0.7649 | 0.9787 |
| 0.0512 | 2.0 | 1334 | 0.0656 | 0.7825 | 0.8412 | 0.8108 | 0.9818 |
| 0.0221 | 3.0 | 2001 | 0.0744 | 0.7908 | 0.8501 | 0.8194 | 0.9822 |
| 0.0107 | 4.0 | 2668 | 0.1022 | 0.7940 | 0.8475 | 0.8199 | 0.9808 |
| 0.008 | 5.0 | 3335 | 0.1055 | 0.7818 | 0.8602 | 0.8191 | 0.9816 |
| 0.0057 | 6.0 | 4002 | 0.1173 | 0.8067 | 0.8590 | 0.832 | 0.9830 |
| 0.0027 | 7.0 | 4669 | 0.1188 | 0.8188 | 0.8501 | 0.8342 | 0.9834 |
| 0.0022 | 8.0 | 5336 | 0.1229 | 0.8080 | 0.8450 | 0.8261 | 0.9826 |
| 0.0019 | 9.0 | 6003 | 0.1341 | 0.8007 | 0.8526 | 0.8258 | 0.9834 |
| 0.0019 | 10.0 | 6670 | 0.1360 | 0.8045 | 0.8628 | 0.8326 | 0.9822 |
| 0.0011 | 11.0 | 7337 | 0.1376 | 0.8163 | 0.8640 | 0.8395 | 0.9838 |
| 0.0008 | 12.0 | 8004 | 0.1447 | 0.8007 | 0.8577 | 0.8282 | 0.9833 |
| 0.0006 | 13.0 | 8671 | 0.1381 | 0.8139 | 0.8615 | 0.8370 | 0.9839 |
| 0.0005 | 14.0 | 9338 | 0.1398 | 0.8297 | 0.8666 | 0.8477 | 0.9843 |
| 0.0004 | 15.0 | 10005 | 0.1404 | 0.8232 | 0.8640 | 0.8431 | 0.9842 |
| 0.0003 | 16.0 | 10672 | 0.1486 | 0.8329 | 0.8551 | 0.8439 | 0.9838 |
| 0.0 | 17.0 | 11339 | 0.1469 | 0.8114 | 0.8691 | 0.8393 | 0.9837 |
| 0.0002 | 18.0 | 12006 | 0.1500 | 0.8297 | 0.8602 | 0.8447 | 0.9843 |
| 0.0001 | 19.0 | 12673 | 0.1489 | 0.8315 | 0.8653 | 0.8481 | 0.9849 |
| 0.0 | 20.0 | 13340 | 0.1484 | 0.8325 | 0.8653 | 0.8486 | 0.9850 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|