Downloads · 30 days
11
2% of all-time downloads
NER_ehealth_Spanish_mBERT_fine_tuned
ajtamayoh/NER_ehealth_Spanish_mBERT_fine_tuned
NER_ehealth_Spanish_mBERT_fine_tuned is a token classification model from ajtamayoh. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
481
Public
Repo size
2.8 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin709 MB · 99%
At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Aug 28, 2022
- Updated
- Aug 28, 2022
- SHA
- 51ac6d08
Try a prompt
Model card
From the Hugging Face model README
<!-- 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. -->
NER_ehealth_Spanish_mBERT_fine_tuned
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6563
- Precision: 0.8094
- Recall: 0.8330
- F1: 0.8210
- Accuracy: 0.9051
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: 12
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 100 | 0.5335 | 0.8018 | 0.8307 | 0.8160 | 0.9047 |
| No log | 2.0 | 200 | 0.5034 | 0.8110 | 0.8253 | 0.8181 | 0.9067 |
| No log | 3.0 | 300 | 0.5632 | 0.7932 | 0.8230 | 0.8078 | 0.9038 |
| No log | 4.0 | 400 | 0.5904 | 0.8004 | 0.8299 | 0.8149 | 0.9027 |
| 0.017 | 5.0 | 500 | 0.5958 | 0.7993 | 0.8330 | 0.8158 | 0.9071 |
| 0.017 | 6.0 | 600 | 0.6168 | 0.7980 | 0.8352 | 0.8162 | 0.9022 |
| 0.017 | 7.0 | 700 | 0.6219 | 0.8079 | 0.8314 | 0.8195 | 0.9062 |
| 0.017 | 8.0 | 800 | 0.6441 | 0.8046 | 0.8299 | 0.8171 | 0.9038 |
| 0.017 | 9.0 | 900 | 0.6338 | 0.8086 | 0.8253 | 0.8168 | 0.9051 |
| 0.0066 | 10.0 | 1000 | 0.6482 | 0.8021 | 0.8261 | 0.8139 | 0.9029 |
| 0.0066 | 11.0 | 1100 | 0.6578 | 0.8039 | 0.8291 | 0.8163 | 0.9038 |
| 0.0066 | 12.0 | 1200 | 0.6563 | 0.8094 | 0.8330 | 0.8210 | 0.9051 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Aug 28, 2022
- Updated
- Aug 28, 2022