Text Classification
Transformers
PyTorch
Safetensors
English
bert
rte
glue
kd
torchdistill
text-embeddings-inference
Instructions to use yoshitomo-matsubara/bert-base-uncased-rte_from_bert-large-uncased-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yoshitomo-matsubara/bert-base-uncased-rte_from_bert-large-uncased-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yoshitomo-matsubara/bert-base-uncased-rte_from_bert-large-uncased-rte")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yoshitomo-matsubara/bert-base-uncased-rte_from_bert-large-uncased-rte") model = AutoModelForSequenceClassification.from_pretrained("yoshitomo-matsubara/bert-base-uncased-rte_from_bert-large-uncased-rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model (#1)
Browse files- Adding `safetensors` variant of this model (0fa471fe14a11e12ddbd19656525e570d082b4e3)
Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>
- .gitattributes +1 -0
- model.safetensors +3 -0
.gitattributes
CHANGED
|
@@ -14,3 +14,4 @@
|
|
| 14 |
*.pb filter=lfs diff=lfs merge=lfs -text
|
| 15 |
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 16 |
*.pth filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 14 |
*.pb filter=lfs diff=lfs merge=lfs -text
|
| 15 |
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 16 |
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 17 |
+
model.safetensors filter=lfs diff=lfs merge=lfs -text
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:48b6ef1f1e8439496526cb2ce7f6052920302c60e3067b8a7886a5069514aefe
|
| 3 |
+
size 437962832
|