---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: mymodel-classify-category-news
  results: []
pipeline_tag: text-classification
---

widget:
- text: "Jens Peter Hansen kommer fra Danmark"
  example_title: "Sentiment analysis"
# mymodel-classify-category-news

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0370
- F1: 0.9443
- Roc Auc: 0.9677
- Accuracy: 0.9401

## Model description

Predict type of Vietnamese news :D 

## Intended uses & limitations
Input limit is 512 tokens so, when model try to predict long text it will error
```
from transformers import pipeline

# Split chunk with 512 token (max_len of tokenizer)
chunk_size = 512
chunks = [prompt[i:i + chunk_size] for i in range(0, len(prompt), chunk_size)]

# pipeline to call model uwu
pipe = pipeline("text-classification", model="duwuonline/mymodel-classify-category-news")

# Create list to save predict
results = []

# Call model to predict small chunk and save them in list
for chunk in chunks:
    result = pipe(chunk)
    results.append(result)

# Function to get most common label
def get_most_common_label(results_list):
    label_counts = {}
    for result in results_list:
        label = result[0]['label']
        label_counts[label] = label_counts.get(label, 0) + 1

    most_common_label = max(label_counts, key=label_counts.get)
    return most_common_label

# call funtion get_most_common_label
most_common_label = get_most_common_label(results)
print("The most label appear is:", most_common_label)

```
## Training and evaluation data

I will update later

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 225  | 0.0466          | 0.9354 | 0.9560  | 0.9157   |
| No log        | 2.0   | 450  | 0.0505          | 0.9215 | 0.9526  | 0.9113   |
| 0.0418        | 3.0   | 675  | 0.0426          | 0.9330 | 0.9607  | 0.9268   |
| 0.0418        | 4.0   | 900  | 0.0397          | 0.9410 | 0.9664  | 0.9379   |
| 0.0202        | 5.0   | 1125 | 0.0370          | 0.9443 | 0.9677  | 0.9401   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3