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---
base_model: SpanBERT/spanbert-base-cased
tags:
- generated_from_trainer
datasets:
- squad
metrics:
- f1
- exact_match
# Optional. Add this if you want to encode your eval results in a structured way.
model-index:
- name: spanbert-base-finetuned-squad
results:
- task:
type: question-answering # Required. Example: automatic-speech-recognition
name: Closed-Domain Question Answering # Optional. Example: Speech Recognition
dataset:
type: squad # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: SQuAD # Required. A pretty name for the dataset. Example: Common Voice (French)
config: squad # Optional. The name of the dataset configuration used in `load_dataset()`. Example: fr in `load_dataset("common_voice", "fr")`. See the `datasets` docs for more info: https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
split: validation # Optional. Example: test
metrics:
- type: exact_match # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 84.6168 # Required. Example: 20.90
name: Exact Match # Optional. Example: Test WER
- type: f1 # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 91.6134 # Required. Example: 20.90
name: F1 # Optional. Example: Test WER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# spanbert-base-finetuned-squad
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.2
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