modularStarEncoder
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ModularStarEncoder-finetuned-27

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 ---
 library_name: transformers
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-# Model Card for Model ID
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 ---
 library_name: transformers
+datasets:
+- bigcode/the-stack-v2
+- modularStarEncoder/SynthCode2Code2NL-neardedup
+license: bigcode-openrail-m
+base_model:
+- modularStarEncoder/ModularStarEncoder
 ---
+# ModularStarEncoder-1B Fine-Tuned model
 <!-- Provide a quick summary of what the model is/does. -->
+ModularStarEncoder-finetuned-27 is an encoder built on top of [ModularStarEncoder-1B Pre-trained](https://huggingface.co/andreagurioli1995/ModularStarEncoder) on [SynthCode2Code2NL](https://huggingface.co/datasets/andreagurioli1995/SynthCode2Code2NL-neardedup).
+ModularStarEncoder fine-tuned-27 is an encoder for various retrieval tasks, enabling the end user to select the model size that meets their memory and computational constraints.
+We built ModularStarEncoder on top of [StarCoder-2](https://huggingface.co/bigcode/starcoder2-15b), reducing its size from 15B to 1B parameters in bfloat16.
+This version contains only the first 27 layers of ModularStarEncoder-finetuned, with the related projection head.
+We have released this version to enhance the model's usability by allowing users to download only the desired size.
+The model is finetuned with [CLIP objective](https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/loss.py)
+- **Paper:** [Link](arxiv.paper)
+- **Languages:** English, Go, Ruby, Python, Java, C++, PHP, C, JavaScript
+### How to use
+```python
+from transformers import AutoModel
+from transformers import AutoTokenizer
+#import the model
+model = AutoModel.from_pretrained("andreagurioli1995/ModularStarEncoder-finetuned", trust_remote_code=True)
+#import the tokenizer, the tokenizer applies LEFT padding!
+tokenizer = AutoTokenizer.from_pretrained("andreagurioli1995/ModularStarEncoder-finetuned")
+language = "yourlanguagelowercased"
+#instruction in case of code embedding in a code language
+instruction_code = f"Represent this {language} code snippet for retrieval:"
+#instruction in case of code embedding in English
+instruction_natural_language = "Represent this code description for retrieving supporting snippets of code:"
+code_snippet = "your code to embed here"
+#You should follow this pattern to embed a snippet of code or natural language queries
+sentence =  f"{tokenizer.sep_token}{instruction_code}{tokenizer.sep_token}{code_snippet)}{tokenizer.cls_token}"
+#Tokenizing your sentence
+tokenized_sensence = tokenizer(sentence, return_tensors="pt",truncation=True, max_length=2048)
+#Embedding the tokenized sentence
+embedded_sentence = model(**sentence)
+```
+You will get as an output three elements:
+- projected_pooled_normalized: a list of the projected, pooled, and normalized embeddings from the five exit points;
+- raw_hidden_states: raw representation from all the hidden states of the model, without pooling, normalization, and projection
+- attentions: attention scores from the encoder
+### Training
+<!-- Provide a longer summary of what this model is. -->
+We fine-tuned ModularStarEncoder with a batch size of 2048 contrastive samples for 20,000 training steps.
+The pre-training and fine-tuning were conducted on 512 NVIDIA Ampere (64GB) GPUs using the [Leonardo](https://arxiv.org/abs/2307.16885) supercomputer, requiring 450,000 GPU working hours.
+| Hyperparameter           | Value     |
+|--------------------------|-----------|
+| Hidden size              | 1024      |
+| Max. position embeddings | 2048      |
+| Num. of attention heads  | 12        |
+| Num. of key values heads | 4         |
+| Num. of hidden layers    | 36        |
+| Attention                | GQA       |
+| Num. of parameters       | ≈1B       |
+|Loss function             |CLIP loss  |
+|Multi-layer loss          | yes       |
+## Licence
+The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).