Downloads · 30 days
12
24% of all-time downloads
bsc-bio-ehr-es-distemist-fasttext-75-ner
Rodrigo1771/bsc-bio-ehr-es-distemist-fasttext-75-ner
bsc-bio-ehr-es-distemist-fasttext-75-ner is a token classification model from Rodrigo1771. 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
51
Public
Parameters
124M
5 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors496 MB · 99%
At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- roberta
- Access
- Public
- Created
- Sep 9, 2024
- Updated
- Sep 9, 2024
- SHA
- 0db0eb9f
Try a prompt
Base models
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. -->
output
This model is a fine-tuned version of PlanTL-GOB-ES/bsc-bio-ehr-es on the Rodrigo1771/distemist-fasttext-75-ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.1711
- Precision: 0.7991
- Recall: 0.8117
- F1: 0.8053
- Accuracy: 0.9759
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1165 | 0.9993 | 702 | 0.0809 | 0.7455 | 0.8039 | 0.7736 | 0.9735 |
| 0.0461 | 2.0 | 1405 | 0.0956 | 0.7611 | 0.8067 | 0.7833 | 0.9747 |
| 0.0165 | 2.9993 | 2107 | 0.1057 | 0.7721 | 0.7990 | 0.7853 | 0.9744 |
| 0.011 | 4.0 | 2810 | 0.1274 | 0.7759 | 0.8196 | 0.7971 | 0.9751 |
| 0.006 | 4.9993 | 3512 | 0.1358 | 0.7904 | 0.8049 | 0.7976 | 0.9745 |
| 0.0045 | 6.0 | 4215 | 0.1420 | 0.7911 | 0.7985 | 0.7948 | 0.9746 |
| 0.0037 | 6.9993 | 4917 | 0.1601 | 0.7925 | 0.8000 | 0.7962 | 0.9749 |
| 0.0022 | 8.0 | 5620 | 0.1621 | 0.8000 | 0.8102 | 0.8051 | 0.9758 |
| 0.0016 | 8.9993 | 6322 | 0.1681 | 0.7972 | 0.8086 | 0.8029 | 0.9758 |
| 0.0013 | 9.9929 | 7020 | 0.1711 | 0.7991 | 0.8117 | 0.8053 | 0.9759 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
- Task
- Token Classification
- Library
- transformers
- Type
- roberta
- License
- apache-2.0
- Created
- Sep 9, 2024
- Updated
- Sep 9, 2024