datasets:
- yelp_review_full
language:
- en
metrics:
- accuracy
- code_eval
library_name: adapter-transformers
Model Card for SentimentTensor
This modelcard provides details about the SentimentTensor model, developed by Saish Shinde, for sentiment analysis using LSTM architecture.
Model Details
Model Description
The SentimentTensor model is a deep learning model based on LSTM architecture, developed by Saish Shinde, for sentiment analysis tasks. It achieves an accuracy of 81% on standard evaluation datasets. The model is designed to classify text data into three categories: negative, neutral, and positive sentiments.
- Developed by: Saish Shinde
- Model type: LSTM-based Sequence Classification
- Language(s) (NLP): English
- License: No specific license
Dataset Used
yelp dataset with 4.04GB compressed,8.65GB uncompressed data
Uses
Direct Use
The SentimentTensor model can be directly used for sentiment analysis tasks without fine-tuning.
Downstream Use
This model can be fine-tuned for specific domains or integrated into larger NLP applications.
Out-of-Scope Use
The model may not perform well on highly specialized or domain-specific text data.
Bias, Risks, and Limitations
The SentimentTensor model, like any LSTM-based model, may have biases and limitations inherent in its training data and architecture. It might sometimes struggle with capturing long-range dependencies or understanding context in complex sentences.
Recommendations
Users should be aware of potential biases and limitations and evaluate results accordingly.
How to Get Started with the Model
Loading the Model
You can load the SentimentTensor model using the Hugging Face library:
python Code:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("your-model-name") tokenizer = AutoTokenizer.from_pretrained("your-tokenizer-name")
Tokenization
text = "Your text data here" tokenized_input = tokenizer(text, return_tensors="pt")
Sentiment Analysis
#Forward pass through the model outputs = model(**tokenized_input)
#Get predicted sentiment label predicted_label = outputs.logits.argmax().item()
Example Usage
#Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("your-model-name") tokenizer = AutoTokenizer.from_pretrained("your-tokenizer-name")
#Tokenize text data
text = "This is a great movie!" tokenized_input = tokenizer(text, return_tensors="pt")
#Perform sentiment analysis
outputs = model(**tokenized_input) predicted_label = outputs.logits.argmax().item()
#Print predicted sentiment sentiment_labels = ["negative", "neutral", "positive"] print(f"Predicted Sentiment: {sentiment_labels[predicted_label]}")
Model Architecture and Objective
The SentimentTensor model is based on LSTM architecture, which is well-suited for sequence classification tasks like sentiment analysis. It uses long short-term memory cells to capture dependencies in sequential data.
Model Card Authors
Saish Shinde