absa_model_v1 / README.md
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---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
datasets:
- __main__
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: absa_model_v1
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: __main__
type: __main__
config: local
split: test
args: local
metrics:
- name: Precision
type: precision
value: 0.4978690430065866
- name: Recall
type: recall
value: 0.5325321176958143
- name: F1
type: f1
value: 0.514617541049259
- name: Accuracy
type: accuracy
value: 0.7477374784110535
---
<!-- 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. -->
# absa_model_v1
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the __main__ dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7541
- Precision: 0.4979
- Recall: 0.5325
- F1: 0.5146
- Accuracy: 0.7477
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7317 | 1.0 | 5905 | 0.7541 | 0.4979 | 0.5325 | 0.5146 | 0.7477 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.15.0