metadata
library_name: transformers
datasets:
- cardiffnlp/tweet_eval
base_model:
- OuteAI/Lite-Oute-1-300M-Instruct
Model Card for Model ID
Model Details
Model Description
OuteAI/Lite-Oute-1-300M-Instruct finetuned on cardiffnlp/tweet_eval for sentiment-analysis task with custom LoRA.
How to Get Started with the Model
Use the code below to get started with the model.
model = AutoModelForCausalLM.from_pretrained(f"efromomr/llm-course-hw3-lora", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(f"efromomr/llm-course-hw3-lora")
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
input_ids = tokenizer(text, return_tensors="pt").input_ids
output_ids = model.generate(input_ids, max_new_tokens=16)
generated_text = tokenizer.decode(output_ids[0][len(input_ids[0]) :], skip_special_tokens=True)
print(generated_text)
#positive
Training Details
Training Data
cardiffnlp/tweet_eval
Evaluation
Testing Data, Factors & Metrics
Metrics
F1: 0.49 on test set