Instructions to use NathanZhu/GabHateCorpusTrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NathanZhu/GabHateCorpusTrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NathanZhu/GabHateCorpusTrained")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NathanZhu/GabHateCorpusTrained") model = AutoModelForSequenceClassification.from_pretrained("NathanZhu/GabHateCorpusTrained") - 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 |
*.msgpack 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 |
*.msgpack 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:a21fee67ab87566ad47a64c361961ac7b691ea714b00ef8a7fa3b10abdd2e1b8
|
| 3 |
+
size 437958648
|