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mymodel-classify-category-news
duwuonline/mymodel-classify-category-news
mymodel-classify-category-news is a text classification model from duwuonline. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
This model is a fine-tuned version of 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
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Jul 24, 2023
- Updated
- Jul 24, 2023
- SHA
- 3fdc0005
Try a prompt
Base models
Model card
From the Hugging Face model README
mymodel-classify-category-news
This model is a fine-tuned version of 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
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Jul 24, 2023
- Updated
- Jul 24, 2023