Text Classification
Transformers
PyTorch
English
bert
social science
covid
text-embeddings-inference
Instructions to use biodatlab/score-claim-identification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biodatlab/score-claim-identification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="biodatlab/score-claim-identification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("biodatlab/score-claim-identification") model = AutoModelForSequenceClassification.from_pretrained("biodatlab/score-claim-identification", 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:c4b95051149ccb11369b926f68c6dfa806160d5932f1304f1ec315d8cdd4eb3e
|
| 3 |
+
size 439707728
|