Instructions to use sohaibdevv/Medical-NER-2026-Success with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sohaibdevv/Medical-NER-2026-Success with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sohaibdevv/Medical-NER-2026-Success")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sohaibdevv/Medical-NER-2026-Success") model = AutoModelForTokenClassification.from_pretrained("sohaibdevv/Medical-NER-2026-Success") - Notebooks
- Google Colab
- Kaggle
Update README.md
#1
by sohaibdevv - opened
README.md
CHANGED
|
@@ -7,6 +7,7 @@ tags:
|
|
| 7 |
model-index:
|
| 8 |
- name: Medical-NER-2026-Success
|
| 9 |
results: []
|
|
|
|
| 10 |
---
|
| 11 |
|
| 12 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
@@ -57,4 +58,4 @@ The following hyperparameters were used during training:
|
|
| 57 |
- Transformers 5.0.0
|
| 58 |
- Pytorch 2.10.0+cpu
|
| 59 |
- Datasets 4.8.3
|
| 60 |
-
- Tokenizers 0.22.2
|
|
|
|
| 7 |
model-index:
|
| 8 |
- name: Medical-NER-2026-Success
|
| 9 |
results: []
|
| 10 |
+
pipeline_tag: token-classification
|
| 11 |
---
|
| 12 |
|
| 13 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
|
|
| 58 |
- Transformers 5.0.0
|
| 59 |
- Pytorch 2.10.0+cpu
|
| 60 |
- Datasets 4.8.3
|
| 61 |
+
- Tokenizers 0.22.2
|