Instructions to use McGill-NLP/dpr-conversation_encoder-basic_info with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use McGill-NLP/dpr-conversation_encoder-basic_info with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="McGill-NLP/dpr-conversation_encoder-basic_info")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/dpr-conversation_encoder-basic_info") model = AutoModel.from_pretrained("McGill-NLP/dpr-conversation_encoder-basic_info") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:024a1506d4d244b54f6f2cee4bc1b75643842ccb1e50710f95375f5a19183078
|
| 3 |
+
size 437961112
|