Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
custom_code
text-embeddings-inference
Instructions to use jwieting/paraphrastic_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jwieting/paraphrastic_test with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jwieting/paraphrastic_test", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use jwieting/paraphrastic_test with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jwieting/paraphrastic_test", trust_remote_code=True) model = AutoModel.from_pretrained("jwieting/paraphrastic_test", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Update modeling_paragram_sp.py
Browse files- modeling_paragram_sp.py +1 -0
modeling_paragram_sp.py
CHANGED
|
@@ -34,4 +34,5 @@ class ParagramSPModel(BertPreTrainedModel):
|
|
| 34 |
embeddings = self.word_embeddings(input_ids)
|
| 35 |
masked_embeddings = embeddings * attention_mask[:, :, None]
|
| 36 |
mean_pooled_embeddings = masked_embeddings.sum(dim=1) / attention_mask[:, :, None].sum(dim=1)
|
|
|
|
| 37 |
return (embeddings, mean_pooled_embeddings, embeddings)
|
|
|
|
| 34 |
embeddings = self.word_embeddings(input_ids)
|
| 35 |
masked_embeddings = embeddings * attention_mask[:, :, None]
|
| 36 |
mean_pooled_embeddings = masked_embeddings.sum(dim=1) / attention_mask[:, :, None].sum(dim=1)
|
| 37 |
+
print(attention_mask[:, :, None].sum(dim=1))
|
| 38 |
return (embeddings, mean_pooled_embeddings, embeddings)
|