tags:
- Named Entity Recognition
- Relation Extraction
datasets:
- text2tech/ner_re_1000_texts_GPT3.5labeled_chat_dataset
- text2tech/ner_100abstracts_100full_texts_GPT4labeled_chat_dataset
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
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Uses
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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
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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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