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---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-finetuned-conll2002
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2002
type: conll2002
config: es
split: validation
args: es
metrics:
- name: Precision
type: precision
value: 0.8175829168559745
- name: Recall
type: recall
value: 0.8269761029411765
- name: F1
type: f1
value: 0.8222526844870915
- name: Accuracy
type: accuracy
value: 0.9739999622092474
---
<!-- 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. -->
# bert-base-cased-finetuned-conll2002
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1670
- Precision: 0.8176
- Recall: 0.8270
- F1: 0.8223
- Accuracy: 0.9740
## Model description
The model described here is a fine-tuned version of the BERT (Bidirectional Encoder Representations from Transformers) base cased model for
Named Entity Recognition (NER) tasks, trained on the CoNLL-2002 dataset. BERT is a pre-trained language model based on the transformer architecture,
designed to understand and process text by considering the context of each word from both directions (left-to-right and right-to-left).
By fine-tuning the BERT base cased model on the CoNLL-2002 dataset, this model has been adapted to recognize and classify named entities such as persons,
organizations, locations, and other miscellaneous entities within Spanish text. The fine-tuning process involves adjusting the pre-trained model
weights to better fit the specific task of NER, thereby improving its performance and accuracy on Spanish text.
## Intended uses & limitations
More information needed
## Training and evaluation data
The training was performed using a GPU with 22.5 GB of RAM, 53 GB of system RAM, and 200 GB of disk space.
This setup ensured efficient handling of the large dataset and the computational demands of fine-tuning the model.
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0248 | 1.0 | 1041 | 0.1439 | 0.8066 | 0.8155 | 0.8110 | 0.9732 |
| 0.0141 | 2.0 | 2082 | 0.1569 | 0.8108 | 0.8182 | 0.8145 | 0.9728 |
| 0.0109 | 3.0 | 3123 | 0.1670 | 0.8176 | 0.8270 | 0.8223 | 0.9740 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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