Instructions to use Sayan01/tiny-bert-mnli-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sayan01/tiny-bert-mnli-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sayan01/tiny-bert-mnli-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sayan01/tiny-bert-mnli-distilled") model = AutoModelForSequenceClassification.from_pretrained("Sayan01/tiny-bert-mnli-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 14
Browse files
logs/events.out.tfevents.1657163824.9181eea70df4.83.4
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 sha256:0061afab5471a2bfc9985e5d7f6e9fdfab585cb5a262fe848677f3e9a45fafdc
|
| 3 |
+
size 10530
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 17562343
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cd7605ade3332fe22ca4a340c9f2e5c1a46229c0c4ae976f4c406a726e1320e6
|
| 3 |
size 17562343
|