Instructions to use Sayan01/tiny-bert-qqp-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sayan01/tiny-bert-qqp-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sayan01/tiny-bert-qqp-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sayan01/tiny-bert-qqp-distilled") model = AutoModelForSequenceClassification.from_pretrained("Sayan01/tiny-bert-qqp-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 10
Browse files
logs/events.out.tfevents.1656010269.8a8318f02c95.81.10
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 sha256:1afc3ba77067219ba25250441ea662e8f28ab1e46661ecf7ce4d7e2a1f9f84f3
|
| 3 |
+
size 8990
|
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 sha256:2b883343b8ae84476b92ead5aaa11e335a6b9a547b37acec4fcefdd891c9c789
|
| 3 |
size 17561831
|