Zero-Shot Classification
Transformers
PyTorch
Safetensors
deberta-v2
text-classification
mdeberta-v3-base
nli
natural-language-inference
multitask
multi-task
pipeline
extreme-multi-task
extreme-mtl
tasksource
zero-shot
rlhf
Instructions to use sileod/mdeberta-v3-base-tasksource-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sileod/mdeberta-v3-base-tasksource-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="sileod/mdeberta-v3-base-tasksource-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sileod/mdeberta-v3-base-tasksource-nli") model = AutoModelForSequenceClassification.from_pretrained("sileod/mdeberta-v3-base-tasksource-nli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b56212743a852862a2285d088e7c6e98adca33bb89431fc0f55cedda03b77a0c
|
| 3 |
+
size 1115275476
|