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---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
model-index:
- name: lora-roberta-large-0927
  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. -->

# lora-roberta-large-0927

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5366
- Accuracy: 0.4472
- Prec: 0.2000
- Recall: 0.4472
- F1: 0.2763
- B Acc: 0.1429
- Micro F1: 0.4472
- Prec Joy: 0.0
- Recall Joy: 0.0
- F1 Joy: 0.0
- Prec Anger: 0.0
- Recall Anger: 0.0
- F1 Anger: 0.0
- Prec Disgust: 0.0
- Recall Disgust: 0.0
- F1 Disgust: 0.0
- Prec Fear: 0.0
- Recall Fear: 0.0
- F1 Fear: 0.0
- Prec Neutral: 0.4472
- Recall Neutral: 1.0
- F1 Neutral: 0.6180
- Prec Sadness: 0.0
- Recall Sadness: 0.0
- F1 Sadness: 0.0
- Prec Surprise: 0.0
- Recall Surprise: 0.0
- F1 Surprise: 0.0

## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 25.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Prec   | Recall | F1     | B Acc  | Micro F1 | Prec Joy | Recall Joy | F1 Joy | Prec Anger | Recall Anger | F1 Anger | Prec Disgust | Recall Disgust | F1 Disgust | Prec Fear | Recall Fear | F1 Fear | Prec Neutral | Recall Neutral | F1 Neutral | Prec Sadness | Recall Sadness | F1 Sadness | Prec Surprise | Recall Surprise | F1 Surprise |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:--------:|:--------:|:----------:|:------:|:----------:|:------------:|:--------:|:------------:|:--------------:|:----------:|:---------:|:-----------:|:-------:|:------------:|:--------------:|:----------:|:------------:|:--------------:|:----------:|:-------------:|:---------------:|:-----------:|
| 0.8381        | 1.25  | 2092  | 1.5415          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4866        | 2.5   | 4184  | 1.5564          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4862        | 3.75  | 6276  | 1.5700          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4762        | 5.0   | 8368  | 1.5391          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4765        | 6.25  | 10460 | 1.5566          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4848        | 7.5   | 12552 | 1.5411          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4782        | 8.75  | 14644 | 1.5548          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4943        | 10.0  | 16736 | 1.6115          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4801        | 11.25 | 18828 | 1.5424          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4946        | 12.5  | 20920 | 1.5637          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4867        | 13.75 | 23012 | 1.5492          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4957        | 15.01 | 25104 | 1.5812          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4913        | 16.26 | 27196 | 1.5425          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.5007        | 17.51 | 29288 | 1.5446          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4919        | 18.76 | 31380 | 1.5616          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4895        | 20.01 | 33472 | 1.5502          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4946        | 21.26 | 35564 | 1.5398          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4754        | 22.51 | 37656 | 1.5307          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |
| 1.4824        | 23.76 | 39748 | 1.5356          | 0.4472   | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472   | 0.0      | 0.0        | 0.0    | 0.0        | 0.0          | 0.0      | 0.0          | 0.0            | 0.0        | 0.0       | 0.0         | 0.0     | 0.4472       | 1.0            | 0.6180     | 0.0          | 0.0            | 0.0        | 0.0           | 0.0             | 0.0         |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3