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

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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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

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

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Training Procedure

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Evaluation

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Summary

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Environmental Impact

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

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