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---
license: apache-2.0
language:
- en
metrics:
- accuracy
pipeline_tag: text2text-generation
tags:
- health
- FHIR
---
# bart-large
This model is a fine-tuned version of [bart-large](https://huggingface.co/facebook/bart-large) on a manually created dataset.
It achieves the following results on the evaluation set:
- Loss: 0.40
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| - | 1.0 | 47 | 4.5156
...
| - | 10 | 490 | 0.4086
## How to use
```python
def generate_text(input_text):
# Tokenize the input text
input_tokens = tokenizer(input_text, return_tensors='pt')
# Move the input tokens to the same device as the model
input_tokens = input_tokens.to(model.device)
# Generate text using the fine-tuned model
output_tokens = model.generate(**input_tokens)
# Decode the generated tokens to text
output_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
return output_text
from transformers import BartForConditionalGeneration
# Load the pre-trained BART model from the Hugging Face model hub
model = BartForConditionalGeneration.from_pretrained('rasta/BART-FHIR-question')
input_text = "List all procedures with reason reference to resource with ID 24680135."
output_text = generate_text(input_text)
print(output_text)
```
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
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