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 5
Browse files
logs/events.out.tfevents.1657985760.d20f26872e62.77.11
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 sha256:9cf361a7665d7e40a0cdb8aa956203da0b922a32cbdd38ef0dce59a70a0bad6d
|
| 3 |
+
size 6185
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 57437959
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6e7af33cc2a8e08c5d80024fdc4dc90f5c8aa608099e50bf126be766bd955c6c
|
| 3 |
size 57437959
|