Instructions to use M-Chimiste/MiniLM-L-12-StackOverflow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M-Chimiste/MiniLM-L-12-StackOverflow with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="M-Chimiste/MiniLM-L-12-StackOverflow")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("M-Chimiste/MiniLM-L-12-StackOverflow") model = AutoModelForMaskedLM.from_pretrained("M-Chimiste/MiniLM-L-12-StackOverflow", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| # Cross-Encoder for MS Marco | |
| This model is a generic masked language model fine tuned on stack overflow data. It's base pre-trained model was the cross-encoder/ms-marco-MiniLM-L-12-v2 model. | |
| The model can be used for creating vectors for search applications. It was trained to be used in conjunction with a knn search with OpenSearch for a pet project I've been working on. It's easiest to create document embeddings with the flair package as shown below. | |
| ## Usage with Transformers | |
| ```python | |
| from flair.data import Sentence | |
| from flair.embeddings import TransformerDocumentEmbeddings | |
| sentence = Sentence("Text to be embedded.") | |
| model = TransformerDocumentEmbeddings("model-name") | |
| model.embed(sentence) | |
| embeddings = sentence.embedding | |
| ``` | |