Instructions to use palakagl/bert_MultiClass_TextClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use palakagl/bert_MultiClass_TextClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="palakagl/bert_MultiClass_TextClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("palakagl/bert_MultiClass_TextClassification") model = AutoModelForSequenceClassification.from_pretrained("palakagl/bert_MultiClass_TextClassification") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2
by SFconvertbot - opened
- .gitattributes +1 -0
- model.safetensors +3 -0
.gitattributes
CHANGED
|
@@ -28,3 +28,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 28 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 29 |
*.tar.gz filter=lfs diff=lfs merge=lfs -text
|
| 30 |
*.pkl filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 28 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 29 |
*.tar.gz filter=lfs diff=lfs merge=lfs -text
|
| 30 |
*.pkl filter=lfs diff=lfs merge=lfs -text
|
| 31 |
+
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:cfb5a778673484e3052cd68ad79c774b788a41b62635aea78e5c92ab4435f415
|
| 3 |
+
size 438153544
|