File size: 3,097 Bytes
2fdb9ef |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 |
---
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: 0.0000
name: Test Accuracy
- type: f1
value: 0.0000
name: F1 Score
- type: precision
value: 0.0000
name: Precision
- type: recall
value: 0.0000
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
```json
{}
```
### Dataset
- **Training samples:** N/A
- **Validation samples:** N/A
- **Test samples:** N/A
## 🚀 Usage
### With Transformers Pipeline
```python
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
```python
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
```json
{}
```
## 📝 Citation
If you use this model, please cite:
```bibtex
@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
- [unknown](https://huggingface.co/unknown)
---
**Generated with MLflow tracking** 🚀
|