Instructions to use Sayan01/tiny-bert-sst2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sayan01/tiny-bert-sst2-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sayan01/tiny-bert-sst2-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sayan01/tiny-bert-sst2-distilled") model = AutoModelForSequenceClassification.from_pretrained("Sayan01/tiny-bert-sst2-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 10
Browse files
logs/events.out.tfevents.1655872621.851641e31c58.72.2
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:b4115aa25c824bc91a7aef9c7f5212887a9c1378c63e7bd32ab6c4a5031a5938
|
| 3 |
+
size 8476
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 38383463
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:64ef6f5a9848e770a4fd93ab1bf8b96a718ed39f0e67062c412082b922d13a3f
|
| 3 |
size 38383463
|