---
base_model: HooshvareLab/bert-base-parsbert-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-parsbert-uncased-ArmanEmo
  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. -->

# finetuned-parsbert-uncased-ArmanEmo

This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0060
- Accuracy: 1.0
- Precision Macro: 1.0
- Recall Macro: 1.0
- F1 Macro: 1.0
- F1 C0: 1.0
- F1 C1: 1.0
- F1 C2: 1.0
- F1 C3: 1.0
- F1 C4: 1.0
- F1 C5: 1.0
- F1 C6: 1.0
- Recall C0: 1.0
- Recall C1: 1.0
- Recall C2: 1.0
- Recall C3: 1.0
- Recall C4: 1.0
- Recall C5: 1.0
- Recall C6: 1.0
- Precision C0: 1.0
- Precision C1: 1.0
- Precision C2: 1.0
- Precision C3: 1.0
- Precision C4: 1.0
- Precision C5: 1.0
- Precision C6: 1.0

## 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: 8
- 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: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 C0  | F1 C1  | F1 C2  | F1 C3  | F1 C4  | F1 C5  | F1 C6  | Recall C0 | Recall C1 | Recall C2 | Recall C3 | Recall C4 | Recall C5 | Recall C6 | Precision C0 | Precision C1 | Precision C2 | Precision C3 | Precision C4 | Precision C5 | Precision C6 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|
| No log        | 1.0   | 144  | 0.3531          | 0.5065   | 0.3687          | 0.3986       | 0.3583   | 0.6766 | 0.3370 | 0.4966 | 0.0    | 0.4164 | 0.0    | 0.5817 | 0.9091    | 0.2366    | 0.7143    | 0.0       | 0.3862    | 0.0       | 0.5440    | 0.5388       | 0.5849       | 0.3806       | 0.0          | 0.4516       | 0.0          | 0.625        |
| No log        | 2.0   | 288  | 0.2289          | 0.7298   | 0.5210          | 0.5631       | 0.5333   | 0.8451 | 0.7790 | 0.6063 | 0.0    | 0.6905 | 0.0    | 0.8120 | 0.9818    | 0.8206    | 0.5       | 0.0       | 0.8       | 0.0       | 0.8394    | 0.7418       | 0.7414       | 0.77         | 0.0          | 0.6073       | 0.0          | 0.7864       |
| No log        | 3.0   | 432  | 0.1129          | 0.9270   | 0.9239          | 0.8955       | 0.9061   | 0.9762 | 0.9492 | 0.8910 | 0.9027 | 0.8792 | 0.8108 | 0.9333 | 0.9709    | 0.9618    | 0.9026    | 0.8947    | 0.9034    | 0.6923    | 0.9430    | 0.9816       | 0.9368       | 0.8797       | 0.9107       | 0.8562       | 0.9783       | 0.9239       |
| 0.3027        | 4.0   | 576  | 0.0567          | 0.9652   | 0.9491          | 0.9545       | 0.9507   | 0.9909 | 0.9847 | 0.9161 | 0.9402 | 0.9565 | 0.8872 | 0.9796 | 0.9927    | 0.9847    | 0.8506    | 0.9649    | 0.9862    | 0.9077    | 0.9948    | 0.9891       | 0.9847       | 0.9924       | 0.9167       | 0.9286       | 0.8676       | 0.9648       |
| 0.3027        | 5.0   | 720  | 0.0296          | 0.9844   | 0.9736          | 0.9819       | 0.9776   | 0.9964 | 0.9885 | 0.9673 | 0.9913 | 0.9862 | 0.9185 | 0.9948 | 0.9927    | 0.9847    | 0.9610    | 1.0       | 0.9862    | 0.9538    | 0.9948    | 1.0          | 0.9923       | 0.9737       | 0.9828       | 0.9862       | 0.8857       | 0.9948       |
| 0.3027        | 6.0   | 864  | 0.0144          | 0.9965   | 0.9939          | 0.9937       | 0.9938   | 0.9982 | 1.0    | 0.9935 | 0.9913 | 0.9965 | 0.9767 | 1.0    | 1.0       | 1.0       | 0.9935    | 1.0       | 0.9931    | 0.9692    | 1.0       | 0.9964       | 1.0          | 0.9935       | 0.9828       | 1.0          | 0.9844       | 1.0          |
| 0.0482        | 7.0   | 1008 | 0.0088          | 0.9991   | 0.9993          | 0.9990       | 0.9991   | 1.0    | 1.0    | 1.0    | 1.0    | 0.9965 | 1.0    | 0.9974 | 1.0       | 1.0       | 1.0       | 1.0       | 0.9931    | 1.0       | 1.0       | 1.0          | 1.0          | 1.0          | 1.0          | 1.0          | 1.0          | 0.9948       |
| 0.0482        | 8.0   | 1152 | 0.0069          | 1.0      | 1.0             | 1.0          | 1.0      | 1.0    | 1.0    | 1.0    | 1.0    | 1.0    | 1.0    | 1.0    | 1.0       | 1.0       | 1.0       | 1.0       | 1.0       | 1.0       | 1.0       | 1.0          | 1.0          | 1.0          | 1.0          | 1.0          | 1.0          | 1.0          |
| 0.0482        | 9.0   | 1296 | 0.0063          | 1.0      | 1.0             | 1.0          | 1.0      | 1.0    | 1.0    | 1.0    | 1.0    | 1.0    | 1.0    | 1.0    | 1.0       | 1.0       | 1.0       | 1.0       | 1.0       | 1.0       | 1.0       | 1.0          | 1.0          | 1.0          | 1.0          | 1.0          | 1.0          | 1.0          |
| 0.0482        | 10.0  | 1440 | 0.0060          | 1.0      | 1.0             | 1.0          | 1.0      | 1.0    | 1.0    | 1.0    | 1.0    | 1.0    | 1.0    | 1.0    | 1.0       | 1.0       | 1.0       | 1.0       | 1.0       | 1.0       | 1.0       | 1.0          | 1.0          | 1.0          | 1.0          | 1.0          | 1.0          | 1.0          |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2