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.1657944267.d20f26872e62.77.5
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:dfe775310c617d34a54ecbe054c5ea9de2d0ec9f4eedbf902b75d21c5edb2746
|
| 3 |
+
size 8865
|
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 sha256:9213a07b2f07df53fbff547daaddb43bf4c1d2e7271894b185fee14f21f51900
|
| 3 |
size 57437959
|