Instructions to use balikasg/SemEval2023Task4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use balikasg/SemEval2023Task4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="balikasg/SemEval2023Task4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("balikasg/SemEval2023Task4") model = AutoModelForSequenceClassification.from_pretrained("balikasg/SemEval2023Task4", device_map="auto") - 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:be48e47d8cf97d9e8d3c89bf476a036623f8a3be66045ae6f861920d86646d8b
|
| 3 |
+
size 6267271608
|