Text Classification
Transformers
PyTorch
Japanese
roberta
zero-shot-classification
nli
Eval Results (legacy)
Instructions to use Formzu/roberta-base-japanese-jsnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Formzu/roberta-base-japanese-jsnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Formzu/roberta-base-japanese-jsnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Formzu/roberta-base-japanese-jsnli") model = AutoModelForSequenceClassification.from_pretrained("Formzu/roberta-base-japanese-jsnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2
by SFconvertbot - opened
- .gitattributes +1 -0
- model.safetensors +3 -0
.gitattributes
CHANGED
|
@@ -30,3 +30,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 30 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 31 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 32 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 30 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 31 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 32 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 33 |
+
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 sha256:249a067bd746e1b38814ea556fbc2aa170b23cf37b8541b57bbfb1cf34a7e640
|
| 3 |
+
size 442513092
|