README.md

---
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

<!-- Provide a quick summary of what the model is/does. -->

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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- **Developed by:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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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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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.

```python
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))
```

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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