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 9
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 sha256:407ca0591d794221937abca474381ea559bcec367b08af557ebe5b1e26be502b
|
| 3 |
+
size 8329
|
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:f7d52127fce9ecd97386ce3e3e8f771d9678e7ea7d0577978ceb2295f54a9eea
|
| 3 |
size 57437959
|