update model card README.md

README.md

@@ -1,9 +1,65 @@
 ---
-library_name: peft
+license: bsd-3-clause
+base_model: Salesforce/codet5p-770m-py
+tags:
+- generated_from_trainer
+datasets:
+- mbpp
+model-index:
+- name: codet5p-770m-py-sanitized-codebleu-1-True-1e-05-0.1-lora-layer_0
+  results: []
 ---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# codet5p-770m-py-sanitized-codebleu-1-True-1e-05-0.1-lora-layer_0
+
+This model is a fine-tuned version of [Salesforce/codet5p-770m-py](https://huggingface.co/Salesforce/codet5p-770m-py) on the mbpp dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9249
+- Codebleu: 0.0072
+- Ngram Match Score: 0.0000
+- Weighted Ngram Match Score: 0.0000
+- Syntax Match Score: 0.0079
+- Dataflow Match Score: 0.0100
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
 ## Training procedure
 
-### Framework versions
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-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
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 1
 
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Codebleu | Ngram Match Score | Weighted Ngram Match Score | Syntax Match Score | Dataflow Match Score |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:--------------------------:|:------------------:|:--------------------:|
+| 0.9688        | 1.0   | 15   | 0.9249          | 0.0072   | 0.0000            | 0.0000                     | 0.0079             | 0.0100               |
+
+
+### Framework versions
 
-- PEFT 0.4.0
+- Transformers 4.31.0
+- Pytorch 2.0.1
+- Datasets 2.14.4
+- Tokenizers 0.13.3