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 8
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 sha256:30b94f08c71ae9980bf9f07cea6423aa5f37bc4851fe9aa61b4b129691b86daa
|
| 3 |
+
size 7515
|
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 sha256:119602e005be7eba36c08681d484e4de90e5be9045990caa095886a5865a8a4a
|
| 3 |
size 17561831
|