Instructions to use Sayan01/tiny-bert-mnli-m-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sayan01/tiny-bert-mnli-m-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sayan01/tiny-bert-mnli-m-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sayan01/tiny-bert-mnli-m-distilled") model = AutoModelForSequenceClassification.from_pretrained("Sayan01/tiny-bert-mnli-m-distilled") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 13
Browse files
logs/events.out.tfevents.1656777391.0dddf6b61e3f.82.10
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 sha256:673c8cabfd681412884156ba2b1935f67f491d4153b0dc196acac22f6cd9e0a6
|
| 3 |
+
size 10136
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 57431751
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:76fabfaf895376afba51b11f0584f5126dee9244be480b47fbd7e17478db680d
|
| 3 |
size 57431751
|