Instructions to use tmills/roberta_sfda_sharpseed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmills/roberta_sfda_sharpseed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmills/roberta_sfda_sharpseed")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmills/roberta_sfda_sharpseed") model = AutoModelForSequenceClassification.from_pretrained("tmills/roberta_sfda_sharpseed") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- .gitattributes +1 -0
- model.safetensors +3 -0
.gitattributes
CHANGED
|
@@ -7,3 +7,4 @@
|
|
| 7 |
*.ot filter=lfs diff=lfs merge=lfs -text
|
| 8 |
*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 9 |
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 7 |
*.ot filter=lfs diff=lfs merge=lfs -text
|
| 8 |
*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 9 |
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
| 10 |
+
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:e9258b2a5c89658512965f9a851ab3e003dcf334ef6756b86efb58460ba825ea
|
| 3 |
+
size 500985736
|