Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use gagan3012/bert-tiny-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gagan3012/bert-tiny-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="gagan3012/bert-tiny-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("gagan3012/bert-tiny-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("gagan3012/bert-tiny-finetuned-ner") - Notebooks
- Google Colab
- Kaggle
Add MIT license
#3
by markvai - opened
README.md
CHANGED
|
@@ -31,6 +31,7 @@ model-index:
|
|
| 31 |
- name: Accuracy
|
| 32 |
type: accuracy
|
| 33 |
value: 0.9597597979252387
|
|
|
|
| 34 |
---
|
| 35 |
|
| 36 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
@@ -87,4 +88,4 @@ The following hyperparameters were used during training:
|
|
| 87 |
- Transformers 4.10.0
|
| 88 |
- Pytorch 1.9.0+cu102
|
| 89 |
- Datasets 1.11.0
|
| 90 |
-
- Tokenizers 0.10.3
|
|
|
|
| 31 |
- name: Accuracy
|
| 32 |
type: accuracy
|
| 33 |
value: 0.9597597979252387
|
| 34 |
+
license: mit
|
| 35 |
---
|
| 36 |
|
| 37 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
|
|
| 88 |
- Transformers 4.10.0
|
| 89 |
- Pytorch 1.9.0+cu102
|
| 90 |
- Datasets 1.11.0
|
| 91 |
+
- Tokenizers 0.10.3
|