Instructions to use NTCAL/SavedAfterTrainingTest39 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTCAL/SavedAfterTrainingTest39 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NTCAL/SavedAfterTrainingTest39")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NTCAL/SavedAfterTrainingTest39") model = AutoModelForSequenceClassification.from_pretrained("NTCAL/SavedAfterTrainingTest39") - Notebooks
- Google Colab
- Kaggle
Commit ·
d1334b6
1
Parent(s): c3c1cb7
Training in progress, epoch 0
Browse files- pytorch_model.bin +1 -1
- training_args.bin +1 -1
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 498163829
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f534722c35d853babbcae0c9fcede54c912d29d735fbd4939e13b929c617d125
|
| 3 |
size 498163829
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 3515
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ddb1d63975b30fd3b70cf27aa48e42d9d1cc5c001c1773b73934a9dec693e3b0
|
| 3 |
size 3515
|