Instructions to use HARSHU550/Bert_IF_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HARSHU550/Bert_IF_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HARSHU550/Bert_IF_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HARSHU550/Bert_IF_Classifier") model = AutoModelForSequenceClassification.from_pretrained("HARSHU550/Bert_IF_Classifier") - Notebooks
- Google Colab
- Kaggle
Upload 2 files
Browse files- flax_model.msgpack +3 -0
- pytorch_model.bin +3 -0
flax_model.msgpack
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8d864a49900962d8f95ce6df87e68fd35dd72f31d92f54ce823d06e1ebd24bfd
|
| 3 |
+
size 437942328
|
pytorch_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:daf55ee85f1607a78c851d0e7ff6fc444b421b436cface3bf521478c22d0beb8
|
| 3 |
+
size 437985387
|