Instructions to use allenai/specter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/specter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="allenai/specter")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("allenai/specter") model = AutoModel.from_pretrained("allenai/specter") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
#6
by thefuzz - opened
README.md
CHANGED
|
@@ -13,7 +13,7 @@ metrics:
|
|
| 13 |
|
| 14 |
## SPECTER
|
| 15 |
|
| 16 |
-
SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a
|
| 17 |
|
| 18 |
Paper: [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://arxiv.org/pdf/2004.07180.pdf)
|
| 19 |
|
|
|
|
| 13 |
|
| 14 |
## SPECTER
|
| 15 |
|
| 16 |
+
SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning.
|
| 17 |
|
| 18 |
Paper: [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://arxiv.org/pdf/2004.07180.pdf)
|
| 19 |
|