Instructions to use modularStarEncoder/ModularStarEncoder-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modularStarEncoder/ModularStarEncoder-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="modularStarEncoder/ModularStarEncoder-finetuned", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("modularStarEncoder/ModularStarEncoder-finetuned", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -47,10 +47,10 @@ code_snippet = "your code to embed here"
|
|
| 47 |
sentence = f"{tokenizer.sep_token}{instruction_code}{tokenizer.sep_token}{code_snippet)}{tokenizer.cls_token}"
|
| 48 |
|
| 49 |
#Tokenizing your sentence
|
| 50 |
-
|
| 51 |
|
| 52 |
#Embedding the tokenized sentence
|
| 53 |
-
embedded_sentence = model(**
|
| 54 |
```
|
| 55 |
|
| 56 |
You will get as an output three elements:
|
|
|
|
| 47 |
sentence = f"{tokenizer.sep_token}{instruction_code}{tokenizer.sep_token}{code_snippet)}{tokenizer.cls_token}"
|
| 48 |
|
| 49 |
#Tokenizing your sentence
|
| 50 |
+
tokenized_sentence = tokenizer(sentence, return_tensors="pt",truncation=True, max_length=2048)
|
| 51 |
|
| 52 |
#Embedding the tokenized sentence
|
| 53 |
+
embedded_sentence = model(**tokenized_sentence)
|
| 54 |
```
|
| 55 |
|
| 56 |
You will get as an output three elements:
|