update model card README.md
Browse files
README.md
ADDED
@@ -0,0 +1,71 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: apache-2.0
|
3 |
+
tags:
|
4 |
+
- generated_from_trainer
|
5 |
+
metrics:
|
6 |
+
- precision
|
7 |
+
- recall
|
8 |
+
- f1
|
9 |
+
- accuracy
|
10 |
+
model-index:
|
11 |
+
- name: NLP-CIC-WFU_SocialDisNER_fine_tuned_NER_EHR_Spanish_model_Mulitlingual_BERT_v2
|
12 |
+
results: []
|
13 |
+
---
|
14 |
+
|
15 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
16 |
+
should probably proofread and complete it, then remove this comment. -->
|
17 |
+
|
18 |
+
# NLP-CIC-WFU_SocialDisNER_fine_tuned_NER_EHR_Spanish_model_Mulitlingual_BERT_v2
|
19 |
+
|
20 |
+
This model is a fine-tuned version of [ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT](https://huggingface.co/ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT) on the None dataset.
|
21 |
+
It achieves the following results on the evaluation set:
|
22 |
+
- Loss: 0.1483
|
23 |
+
- Precision: 0.8699
|
24 |
+
- Recall: 0.8722
|
25 |
+
- F1: 0.8711
|
26 |
+
- Accuracy: 0.9771
|
27 |
+
|
28 |
+
## Model description
|
29 |
+
|
30 |
+
More information needed
|
31 |
+
|
32 |
+
## Intended uses & limitations
|
33 |
+
|
34 |
+
More information needed
|
35 |
+
|
36 |
+
## Training and evaluation data
|
37 |
+
|
38 |
+
More information needed
|
39 |
+
|
40 |
+
## Training procedure
|
41 |
+
|
42 |
+
### Training hyperparameters
|
43 |
+
|
44 |
+
The following hyperparameters were used during training:
|
45 |
+
- learning_rate: 5e-05
|
46 |
+
- train_batch_size: 8
|
47 |
+
- eval_batch_size: 8
|
48 |
+
- seed: 42
|
49 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
50 |
+
- lr_scheduler_type: linear
|
51 |
+
- num_epochs: 7
|
52 |
+
|
53 |
+
### Training results
|
54 |
+
|
55 |
+
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|
56 |
+
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
|
57 |
+
| No log | 1.0 | 467 | 0.0851 | 0.8415 | 0.8209 | 0.8310 | 0.9720 |
|
58 |
+
| 0.1011 | 2.0 | 934 | 0.1034 | 0.8681 | 0.8464 | 0.8571 | 0.9744 |
|
59 |
+
| 0.0537 | 3.0 | 1401 | 0.1094 | 0.8527 | 0.8608 | 0.8568 | 0.9753 |
|
60 |
+
| 0.0335 | 4.0 | 1868 | 0.1239 | 0.8617 | 0.8603 | 0.8610 | 0.9751 |
|
61 |
+
| 0.0185 | 5.0 | 2335 | 0.1192 | 0.8689 | 0.8627 | 0.8658 | 0.9756 |
|
62 |
+
| 0.0112 | 6.0 | 2802 | 0.1426 | 0.8672 | 0.8663 | 0.8667 | 0.9765 |
|
63 |
+
| 0.0067 | 7.0 | 3269 | 0.1483 | 0.8699 | 0.8722 | 0.8711 | 0.9771 |
|
64 |
+
|
65 |
+
|
66 |
+
### Framework versions
|
67 |
+
|
68 |
+
- Transformers 4.20.1
|
69 |
+
- Pytorch 1.11.0+cu113
|
70 |
+
- Datasets 2.3.2
|
71 |
+
- Tokenizers 0.12.1
|