mistral-7b-instruct-v0.2-NER-RE-qlora-1200docs

Model Card for mistral-7b-instruct-v0.2-NER-RE-qlora-1200docs

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Mistral fine-tuned on 1000 GPT3.5- and 200 GPT4-labeled documents to extract technical entities and relations between entities from texts.

Model Details

Model Description

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Uses

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Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets

# load model and tokenizer
MODEL = "text2tech/mistral-7b-instruct-v0.2-NER-RE-qlora-1200docs"
model = AutoModelForCausalLM.from_pretrained(MODEL, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(MODEL, padding_side="left", pad_token_id=0)

# prepare example data
data = datasets.load_dataset("text2tech/ner_re_1000_texts_GPT3.5labeled_chat_dataset")
ex_user_prompt = [data['test']['NER_chats'][0][0]]
ex = tokenizer.apply_chat_template(ex_user_prompt, add_generation_prompt=True, return_dict=True, return_tensors='pt')
ex = {k: v.to(model.device) for k, v in ex.items()} 
print(ex_user_prompt[0]['content'])

# generate response
response = model.generate(**ex, max_new_tokens=300, temperature=0.0)

# print decoded 
input_len = ex['input_ids'].shape[1]
print(tokenizer.decode(response[0][input_len:], skip_special_tokens=True))

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

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Results

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Citation [optional]

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Glossary [optional]

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