Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ericntay/ft_clinical_bert_diabetes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ericntay/ft_clinical_bert_diabetes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ericntay/ft_clinical_bert_diabetes")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ericntay/ft_clinical_bert_diabetes") model = AutoModelForSequenceClassification.from_pretrained("ericntay/ft_clinical_bert_diabetes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#2
by librarian-bot - opened
README.md
CHANGED
|
@@ -5,6 +5,7 @@ tags:
|
|
| 5 |
metrics:
|
| 6 |
- accuracy
|
| 7 |
- f1
|
|
|
|
| 8 |
model-index:
|
| 9 |
- name: ft_clinical_bert_diabetes
|
| 10 |
results: []
|
|
|
|
| 5 |
metrics:
|
| 6 |
- accuracy
|
| 7 |
- f1
|
| 8 |
+
base_model: emilyalsentzer/Bio_ClinicalBERT
|
| 9 |
model-index:
|
| 10 |
- name: ft_clinical_bert_diabetes
|
| 11 |
results: []
|