Instructions to use voidism/diffcse-bert-base-uncased-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidism/diffcse-bert-base-uncased-sts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="voidism/diffcse-bert-base-uncased-sts")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("voidism/diffcse-bert-base-uncased-sts") model = AutoModel.from_pretrained("voidism/diffcse-bert-base-uncased-sts") - Notebooks
- Google Colab
- Kaggle
update model name
Browse files- config.json +2 -2
config.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "diffcse-bert-base-uncased-sts",
|
| 3 |
"architectures": [
|
| 4 |
-
"
|
| 5 |
],
|
| 6 |
"attention_probs_dropout_prob": 0.1,
|
| 7 |
"gradient_checkpointing": false,
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "voidism/diffcse-bert-base-uncased-sts",
|
| 3 |
"architectures": [
|
| 4 |
+
"BertModel"
|
| 5 |
],
|
| 6 |
"attention_probs_dropout_prob": 0.1,
|
| 7 |
"gradient_checkpointing": false,
|