distilgpt2-finetuned-microbiology

distilgpt2-finetuned-microbiology

Model description

Small model for language modeling based on distilgpt2 and on microbiology-related text data. It achieves the following results on the evaluation set:

• Loss: 2.1073

Intended uses & limitations

This model was finetuned solely for academic purposes, specifically:

• Notes enhancement
• Study
• Research

Keep in mind that the model itself does not always provide correct informtion, so always double check everything.

distilgpt2-finetuned-microbiology must not be used for medical/health purposes, as it was not trained for that.

Besides the limitations already highlighted for distilgpt2, distilgpt2-finetuned-microbiology was trained on a small microbiology-related texts dataset, so its knowledge is not nearly as comprehensive as many other sources of information. It is still useful when employed as assistant, not as substitute of human researchers/experts.

Training and evaluation data

Training data were taken from Biology dataset on HuggingFace, and microbiology texts were extracted from the .parquet file associated with this dataset, following this workflow:

Data preprocessing and extraction

Find all files and scripts on GitHub:

# UNZIP LARGE DATA FILES
gzip -d data/*.gz
# CONVERT .parquet FILE TO .jsonl
python3 scripts/parquet_to_jsonl.py
# FILTER MICROBIOLOGY TEXTS FROM microbiology.jsonl
python3 scripts/data_preprocess.py

Training procedure

Training procedure is as descripted by this HuggingFace notebook.

You can find the related script on GitHub.

You only have to run this command, once you preprocessed and extracted everything.

#GENERATE MODEL
python3 scripts/build_distilgpt2-finetuned-microbiology.py

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2
• accelerate 0.27.2
• scikit-learn 1.2.2
• huggingface_hub 0.20.3

Use the model in python

Here is a snippet code on how to load the model in python:

model_checkpoint = "as-cle-bert/distilgpt2-finetuned-microbiology"

# Load necessary dependencies
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForCausalLM.from_pretrained(model_checkpoint)

References

• HuggingFace notebook - template for building distilgpt2-finetuned-microbiology
• Biology dataset on HuggingFace - microbiology texts were extracted from the .parquet file associated with this dataset and put in microbiology.jsonl