Instructions to use Harini2506/scibert_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harini2506/scibert_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Harini2506/scibert_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Harini2506/scibert_1") model = AutoModelForTokenClassification.from_pretrained("Harini2506/scibert_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
97fa24f
1
Parent(s): a9abfdc
Training in progress, epoch 6
Browse files- pytorch_model.bin +1 -1
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 554521634
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:466fc1c5f70a03ff4d69c59e3e821e5bff558ea5607283c7dec2893d702b7613
|
| 3 |
size 554521634
|