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 the adversarialQA config and validation split of adversarial_qa
#3
by autoevaluator HF Staff - opened
README.md
CHANGED
|
@@ -28,6 +28,23 @@ model-index:
|
|
| 28 |
type: f1
|
| 29 |
value: 87.606
|
| 30 |
verified: true
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 31 |
---
|
| 32 |
|
| 33 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
|
|
| 28 |
type: f1
|
| 29 |
value: 87.606
|
| 30 |
verified: true
|
| 31 |
+
- task:
|
| 32 |
+
type: question-answering
|
| 33 |
+
name: Question Answering
|
| 34 |
+
dataset:
|
| 35 |
+
name: adversarial_qa
|
| 36 |
+
type: adversarial_qa
|
| 37 |
+
config: adversarialQA
|
| 38 |
+
split: validation
|
| 39 |
+
metrics:
|
| 40 |
+
- name: Exact Match
|
| 41 |
+
type: exact_match
|
| 42 |
+
value: 19.1
|
| 43 |
+
verified: true
|
| 44 |
+
- name: F1
|
| 45 |
+
type: f1
|
| 46 |
+
value: 29.8017
|
| 47 |
+
verified: true
|
| 48 |
---
|
| 49 |
|
| 50 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|