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model-index:
- name: LLAMA 7B Sentiment Analysis Adapter
results:
- task:
name: Sentiment Analysis
type: text-classification
dataset:
name: Amazon Sentiment Review dataset
type: amazon_reviews
model-metadata:
license: apache-2.0
library_name: transformers
tags: ["text-classification", "sentiment-analysis", "English"]
languages: ["en"]
widget:
- text: "I love using FuturixAI for my daily tasks!"
intended-use:
primary-uses:
- This model is intended for sentiment analysis on English language text.
primary-users:
- Researchers
- Social media monitoring tools
- Customer feedback analysis systems
training-data:
training-data-source: Amazon Sentiment Review dataset
quantitative-analyses:
use-cases-limitations:
- The model may perform poorly on texts that contain a lot of slang or are in a different language than it was trained on.
ethical-considerations:
risks-and-mitigations:
- There is a risk of the model reinforcing or creating biases based on the training data. Users should be aware of this and consider additional bias mitigation strategies when using the model.
model-architecture:
architecture: LLAMA 7B with LORA adaptation
library: PeftModel
how-to-use:
installation:
- pip install transformers peft
code-examples:
- |
```python
import transformers
from peft import PeftModel
model_name = "meta-llama/Llama-2-7b" # you can use VICUNA 7B model as well
peft_model_id = "Futurix-AI/LLAMA_7B_Sentiment_Analysis_Amazon_Review_Dataset"
tokenizer_t5 = transformers.AutoTokenizer.from_pretrained(model_name)
model_t5 = transformers.AutoModelForCausalLM.from_pretrained(model_name)
model_t5 = PeftModel.from_pretrained(model_t5, peft_model_id)
prompt = """
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
###Instruction:
Detect the sentiment of the tweet.
###Input:
FuturixAI embodies the spirit of innovation, with a resolve to push the boundaries of what's possible through science and technology.
###Response:
"""
inputs = tokenizer_t5(prompt, return_tensors="pt")
for k, v in inputs.items():
inputs[k] = v
outputs = model_t5.generate(**inputs, max_length=256, do_sample=True)
text = tokenizer_t5.batch_decode(outputs, skip_special_tokens=True)[0]
print(text)
```
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