---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- finer-139
- nlpaueb/finer-139
metrics:
- precision
- recall
- f1
- accuracy
base_model: google/bert_uncased_L-2_H-128_A-2
model-index:
- name: bertiny-finetuned-finer
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: finer-139
      type: finer-139
      args: finer-139
    metrics:
    - type: precision
      value: 0.5339285714285714
      name: Precision
    - type: recall
      value: 0.036011080332409975
      name: Recall
    - type: f1
      value: 0.06747151077513258
      name: F1
    - type: accuracy
      value: 0.9847166143263048
      name: Accuracy
---

<!-- 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. -->

# bertiny-finetuned-finer

This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the finer-139 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0882
- Precision: 0.5339
- Recall: 0.0360
- F1: 0.0675
- Accuracy: 0.9847

## 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: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0871        | 1.0   | 11255 | 0.0952          | 0.0       | 0.0    | 0.0    | 0.9843   |
| 0.0864        | 2.0   | 22510 | 0.0895          | 0.7640    | 0.0082 | 0.0162 | 0.9844   |
| 0.0929        | 3.0   | 33765 | 0.0882          | 0.5339    | 0.0360 | 0.0675 | 0.9847   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1