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--- |
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library_name: transformers |
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datasets: |
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- cardiffnlp/tweet_eval |
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base_model: |
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- OuteAI/Lite-Oute-1-300M-Instruct |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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OuteAI/Lite-Oute-1-300M-Instruct finetuned on cardiffnlp/tweet_eval for sentiment-analysis task with custom LoRA. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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model = AutoModelForCausalLM.from_pretrained(f"efromomr/llm-course-hw3-lora", device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained(f"efromomr/llm-course-hw3-lora") |
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tokenizer.pad_token = tokenizer.eos_token |
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tokenizer.padding_side = "left" |
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input_ids = tokenizer(text, return_tensors="pt").input_ids |
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output_ids = model.generate(input_ids, max_new_tokens=16) |
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generated_text = tokenizer.decode(output_ids[0][len(input_ids[0]) :], skip_special_tokens=True) |
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print(generated_text) |
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#positive |
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``` |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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cardiffnlp/tweet_eval |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Metrics |
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F1: 0.49 on test set |
