Instructions to use Sayan01/tiny-bert-mrpc-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sayan01/tiny-bert-mrpc-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sayan01/tiny-bert-mrpc-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sayan01/tiny-bert-mrpc-distilled") model = AutoModelForSequenceClassification.from_pretrained("Sayan01/tiny-bert-mrpc-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 7
Browse files
logs/events.out.tfevents.1655831357.4dede0cb6010.74.26
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 sha256:2cd9737ee82b413ee724ca3dcae621aa61ecd085d017616d60479ba4dd6b7f37
|
| 3 |
+
size 7035
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 17561831
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b0b632a6dbfc85c041977da9449f80e4120ae4a7a6bd2a866d058b82777ab925
|
| 3 |
size 17561831
|