Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use peter2000/tsdae_model_policy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use peter2000/tsdae_model_policy with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("peter2000/tsdae_model_policy") 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 peter2000/tsdae_model_policy with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("peter2000/tsdae_model_policy") model = AutoModel.from_pretrained("peter2000/tsdae_model_policy") - Notebooks
- Google Colab
- Kaggle
Pushing tsdae fine tuned model
Browse filesModel trained on 500k policy sentencey
config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"__version__": {
|
| 3 |
+
"sentence_transformers": "2.2.2",
|
| 4 |
+
"transformers": "4.27.2",
|
| 5 |
+
"pytorch": "1.13.1+cu116"
|
| 6 |
+
}
|
| 7 |
+
}
|