Question Answering
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use mrp/bert-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrp/bert-finetuned-squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="mrp/bert-finetuned-squad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mrp/bert-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("mrp/bert-finetuned-squad") - Notebooks
- Google Colab
- Kaggle
Add evaluation results on squad dataset
#1
by autoevaluator HF Staff - opened
README.md
CHANGED
|
@@ -6,7 +6,20 @@ datasets:
|
|
| 6 |
- squad
|
| 7 |
model-index:
|
| 8 |
- name: bert-finetuned-squad
|
| 9 |
-
results:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 10 |
---
|
| 11 |
|
| 12 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
|
|
| 6 |
- squad
|
| 7 |
model-index:
|
| 8 |
- name: bert-finetuned-squad
|
| 9 |
+
results:
|
| 10 |
+
- task:
|
| 11 |
+
type: question-answering
|
| 12 |
+
name: Question Answering
|
| 13 |
+
dataset:
|
| 14 |
+
name: squad
|
| 15 |
+
type: squad
|
| 16 |
+
config: plain_text
|
| 17 |
+
split: validation
|
| 18 |
+
metrics:
|
| 19 |
+
- name: Loss
|
| 20 |
+
type: loss
|
| 21 |
+
value: 1.073493242263794
|
| 22 |
+
verified: true
|
| 23 |
---
|
| 24 |
|
| 25 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|