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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-cased-cv-studio_name-pooler
  results: []
---

<!-- 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-cv-studio_name-pooler

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9427
- Accuracy: 0.3881
- F1 Micro: 0.3881
- F1 Macro: 0.1563
- Precision Micro: 0.3881
- Recall Micro: 0.3881

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1 Micro | F1 Macro | Precision Micro | Recall Micro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|:------------:|
| 2.3164        | 0.98  | 1000  | 2.2860          | 0.2556   | 0.2556   | 0.0327   | 0.2556          | 0.2556       |
| 2.2301        | 1.96  | 2000  | 2.1948          | 0.3052   | 0.3052   | 0.0791   | 0.3052          | 0.3052       |
| 2.1534        | 2.93  | 3000  | 2.1321          | 0.3179   | 0.3179   | 0.0811   | 0.3179          | 0.3179       |
| 2.092         | 3.91  | 4000  | 2.0889          | 0.3392   | 0.3392   | 0.1002   | 0.3392          | 0.3392       |
| 2.0748        | 4.89  | 5000  | 2.0511          | 0.3541   | 0.3541   | 0.1108   | 0.3541          | 0.3541       |
| 2.0555        | 5.87  | 6000  | 2.0292          | 0.3602   | 0.3602   | 0.1111   | 0.3602          | 0.3602       |
| 2.0416        | 6.84  | 7000  | 2.0080          | 0.3715   | 0.3715   | 0.1287   | 0.3715          | 0.3715       |
| 2.0162        | 7.82  | 8000  | 1.9921          | 0.3663   | 0.3663   | 0.1240   | 0.3663          | 0.3663       |
| 1.9931        | 8.8   | 9000  | 1.9805          | 0.3746   | 0.3746   | 0.1431   | 0.3746          | 0.3746       |
| 1.9644        | 9.78  | 10000 | 1.9660          | 0.3805   | 0.3805   | 0.1468   | 0.3805          | 0.3805       |
| 1.9664        | 10.75 | 11000 | 1.9573          | 0.3815   | 0.3815   | 0.1461   | 0.3815          | 0.3815       |
| 1.9606        | 11.73 | 12000 | 1.9508          | 0.3842   | 0.3842   | 0.1505   | 0.3842          | 0.3842       |
| 1.9666        | 12.71 | 13000 | 1.9489          | 0.3859   | 0.3859   | 0.1583   | 0.3859          | 0.3859       |
| 1.9507        | 13.69 | 14000 | 1.9435          | 0.3851   | 0.3851   | 0.1530   | 0.3851          | 0.3851       |
| 1.9522        | 14.66 | 15000 | 1.9427          | 0.3881   | 0.3881   | 0.1563   | 0.3881          | 0.3881       |


### Framework versions

- Transformers 4.19.0
- Pytorch 1.8.2+cu111
- Datasets 1.6.2
- Tokenizers 0.12.1
