File size: 2,562 Bytes
6349453 74cc77c 6349453 74cc77c 6349453 74cc77c 6349453 74cc77c 6349453 74cc77c 6349453 74cc77c 6349453 74cc77c 6349453 |
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 |
---
library_name: transformers
base_model: dccuchile/bert-base-spanish-wwm-cased
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: beto-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: biobert_json
type: biobert_json
config: Biobert_json
split: validation
args: Biobert_json
metrics:
- name: Precision
type: precision
value: 0.9479897894065092
- name: Recall
type: recall
value: 0.9685937839600087
- name: F1
type: f1
value: 0.9581810363362717
- name: Accuracy
type: accuracy
value: 0.9781675579322638
---
<!-- 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. -->
# beto-finetuned-ner
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1032
- Precision: 0.9480
- Recall: 0.9686
- F1: 0.9582
- Accuracy: 0.9782
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3605 | 1.0 | 612 | 0.1104 | 0.9414 | 0.9462 | 0.9438 | 0.9707 |
| 0.1106 | 2.0 | 1224 | 0.1074 | 0.9306 | 0.9707 | 0.9502 | 0.9742 |
| 0.0786 | 3.0 | 1836 | 0.0983 | 0.9460 | 0.9688 | 0.9573 | 0.9776 |
| 0.0596 | 4.0 | 2448 | 0.1017 | 0.9465 | 0.9703 | 0.9583 | 0.9780 |
| 0.0387 | 5.0 | 3060 | 0.1032 | 0.9480 | 0.9686 | 0.9582 | 0.9782 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|