metadata

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: "Is this review positive or negative? Review: Best cast iron skillet you will ever buy." example_title: "Sentiment analysis"
text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had ..." example_title: "Coreference resolution"
text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book ..." example_title: "Logic puzzles"
text: "The two men running to become New York City's next mayor will face off in their first debate Wednesday night ..." example_title: "Reading comprehension"

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