Instructions to use howey/bert-base-uncased-boolq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use howey/bert-base-uncased-boolq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="howey/bert-base-uncased-boolq")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("howey/bert-base-uncased-boolq") model = AutoModelForSequenceClassification.from_pretrained("howey/bert-base-uncased-boolq") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- .gitattributes +1 -0
- model.safetensors +3 -0
.gitattributes
CHANGED
|
@@ -15,3 +15,4 @@
|
|
| 15 |
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 16 |
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 17 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 15 |
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 16 |
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 17 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 18 |
+
model.safetensors filter=lfs diff=lfs merge=lfs -text
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a2f9f58164140182868a0df84e1a7a75dff566de0691e7c7938ada1c80d56fb4
|
| 3 |
+
size 437962832
|