Instructions to use Sayan01/tiny-bert-mnli-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sayan01/tiny-bert-mnli-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sayan01/tiny-bert-mnli-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sayan01/tiny-bert-mnli-distilled") model = AutoModelForSequenceClassification.from_pretrained("Sayan01/tiny-bert-mnli-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 2
Browse files
logs/events.out.tfevents.1655828616.4dede0cb6010.74.22
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b30bae747a6584da51d6b6d33c9abdc33fa066c52170c87f2ef3db3ace23490e
|
| 3 |
+
size 4635
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 17561831
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:00959899b2f3a52705e8ed79dd0081702d0b7acc251a20cc5cff6217f5fe5402
|
| 3 |
size 17561831
|