license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: suarkadipa/GPT-2-finetuned-medical-domain
results: []
datasets:
- argilla/medical-domain
suarkadipa/GPT-2-finetuned-medical-domain
This model is a fine-tuned version of distilgpt2 on an argilla/medical-domain dataset. Based on https://python.plainenglish.io/i-fine-tuned-gpt-2-on-100k-scientific-papers-heres-the-result-903f0784fe65
It achieves the following results on the evaluation set:
- Train Loss: 2.5822
- Validation Loss: 2.1133
- Epoch: 0
Model description
More information needed
Intended uses & limitations
How to run in Google Colab
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer_fromhub = AutoTokenizer.from_pretrained("suarkadipa/GPT-2-finetuned-papers")
model_fromhub = AutoModelForCausalLM.from_pretrained("suarkadipa/GPT-2-finetuned-papers", from_tf=True)
text_generator = pipeline( "text-generation", model=model_fromhub, tokenizer=tokenizer_fromhub, framework="tf", max_new_tokens=3000 )
// change with your text
test_sentence = "the lungs"
res=text_generator(test_sentence)[0]["generated_text"].replace("\n", " ")
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 500, 'decay_rate': 0.95, 'staircase': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
Train Loss | Validation Loss | Epoch |
---|---|---|
2.5822 | 2.1133 | 0 |
Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3