Update README.md

README.md

@@ -8,4 +8,66 @@ 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
+