Instructions to use oliverqq/scibert-uncased-topics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oliverqq/scibert-uncased-topics with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oliverqq/scibert-uncased-topics")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oliverqq/scibert-uncased-topics") model = AutoModelForSequenceClassification.from_pretrained("oliverqq/scibert-uncased-topics") - Notebooks
- Google Colab
- Kaggle
Add TF weights
#1
by joaogante - opened
- tf_model.h5 +3 -0
tf_model.h5
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1af7d59eff2e0e97e3443e2128bc1a7e7a6d20466c32027806208d754a37632d
|
| 3 |
+
size 439986480
|