Text Classification
Transformers
PyTorch
English
bert
Eval Results (legacy)
text-embeddings-inference
Instructions to use MattStammers/Statement_Equivalence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MattStammers/Statement_Equivalence with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MattStammers/Statement_Equivalence")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MattStammers/Statement_Equivalence") model = AutoModelForSequenceClassification.from_pretrained("MattStammers/Statement_Equivalence") - 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:dd325fe6e0596f7eb2a6979e6f1a12a623619f50b988798707469b5af9ec8536
|
| 3 |
+
size 437958648
|