metadata
language: en
license: apache-2.0
tags:
- sentiment-analysis
- transformers
- unknown
- text-classification
datasets:
- unknown
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: unknown-sentiment
results:
- task:
type: text-classification
name: Sentiment Analysis
dataset:
name: UNKNOWN
type: unknown
metrics:
- type: accuracy
value: 0
name: Test Accuracy
- type: f1
value: 0
name: F1 Score
- type: precision
value: 0
name: Precision
- type: recall
value: 0
name: Recall
UNKNOWN Fine-tuned for Sentiment Analysis
📊 Model Description
This model is a fine-tuned version of unknown for sentiment analysis on the UNKNOWN dataset.
Model Architecture: unknown
Task: Binary Sentiment Classification (Positive/Negative)
Language: English
Training Date: N/A
🎯 Performance Metrics
| Metric | Score |
|---|---|
| Accuracy | 0.0000 |
| F1 Score | 0.0000 |
| Precision | 0.0000 |
| Recall | 0.0000 |
| Loss | 0.0000 |
🔧 Training Details
Hyperparameters
{}
Dataset
- Training samples: N/A
- Validation samples: N/A
- Test samples: N/A
🚀 Usage
With Transformers Pipeline
from transformers import pipeline
# Load the model
classifier = pipeline("sentiment-analysis", model="YOUR_USERNAME/YOUR_MODEL_NAME")
# Predict
result = classifier("I love this movie!")
print(result)
# [{'label': 'POSITIVE', 'score': 0.9998}]
Manual Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
model_name = "YOUR_USERNAME/YOUR_MODEL_NAME"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Prepare input
text = "This is an amazing product!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
# Predict
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
# Get result
label_id = torch.argmax(predictions).item()
score = predictions[0][label_id].item()
labels = ["NEGATIVE", "POSITIVE"]
print(f"Label: {labels[label_id]}, Score: {score:.4f}")
📈 Training Curves
Training history visualization is available in the model files.
🏷️ Label Mapping
0: NEGATIVE
1: POSITIVE
⚙️ Model Configuration
{}
📝 Citation
If you use this model, please cite:
@misc{sentiment-model-unknown,
author = {Your Name},
title = {unknown Fine-tuned for Sentiment Analysis},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/YOUR_USERNAME/YOUR_MODEL_NAME}}
}
🤝 Contact
For questions or feedback, please open an issue in the repository.
📄 License
Apache 2.0
🔗 Related Models
Generated with MLflow tracking 🚀