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# result

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.

## Model description

The model was trained with the NaturalQuestions dataset from https://github.com/mrqa/MRQA-Shared-Task-2019

## Intended uses & limitations

This fine-tuned model is used in the context of the NLP4Web course at TU Darmstadt

## Training and evaluation data

The NaturalQuestions dataset from https://github.com/mrqa/MRQA-Shared-Task-2019 was used


## Training procedure

The pytorch question-answering/run_qa.py script was used to fine tune the model.
The following parameters were passed to the script 
  --model_name_or_path distilbert-base-uncased \
  --train_file $TRAIN_FILE_NAME \
  --validation_file $EVAL_FILE_NAME \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 12 \
  --learning_rate 3e-5 \
  --num_train_epochs 2 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir ./tmp/result/

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0

### Training results

***** train metrics *****
  epoch                    =        2.0
  train_loss               =     0.9724
  train_runtime            = 2:57:05.43
  train_samples            =     148108
  train_samples_per_second =     27.878
  train_steps_per_second   =      2.323

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "result", "results": []}]}
transformers pytorch distilbert question-answering generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 




# result

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.

## Model description

The model was trained with the NaturalQuestions dataset from URL

## Intended uses & limitations

This fine-tuned model is used in the context of the NLP4Web course at TU Darmstadt

## Training and evaluation data

The NaturalQuestions dataset from URL was used


## Training procedure

The pytorch question-answering/run_qa.py script was used to fine tune the model.
The following parameters were passed to the script 
  --model_name_or_path distilbert-base-uncased \
  --train_file $TRAIN_FILE_NAME \
  --validation_file $EVAL_FILE_NAME \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 12 \
  --learning_rate 3e-5 \
  --num_train_epochs 2 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir ./tmp/result/

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0

### Training results

* train metrics *
  epoch                    =        2.0
  train_loss               =     0.9724
  train_runtime            = 2:57:05.43
  train_samples            =     148108
  train_samples_per_second =     27.878
  train_steps_per_second   =      2.323

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

# result

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. ## Model description

The model was trained with the NaturalQuestions dataset from URL ## Intended uses & limitations

This fine-tuned model is used in the context of the NLP4Web course at TU Darmstadt ## Training and evaluation data

The NaturalQuestions dataset from URL was used ## Training procedure

The pytorch question-answering/run_qa.py script was used to fine tune the model.
The following parameters were passed to the script 
  --model_name_or_path distilbert-base-uncased \
  --train_file $TRAIN_FILE_NAME \
  --validation_file $EVAL_FILE_NAME \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 12 \
  --learning_rate 3e-5 \
  --num_train_epochs 2 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir ./tmp/result/ ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0 ### Training results

* train metrics *
  epoch                    =        2.0
  train_loss               =     0.9724
  train_runtime            = 2:57:05.43
  train_samples            =     148108
  train_samples_per_second =     27.878
  train_steps_per_second   =      2.323 ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 # result

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. ## Model description

The model was trained with the NaturalQuestions dataset from URL ## Intended uses & limitations

This fine-tuned model is used in the context of the NLP4Web course at TU Darmstadt ## Training and evaluation data

The NaturalQuestions dataset from URL was used ## Training procedure

The pytorch question-answering/run_qa.py script was used to fine tune the model.
The following parameters were passed to the script 
  --model_name_or_path distilbert-base-uncased \
  --train_file $TRAIN_FILE_NAME \
  --validation_file $EVAL_FILE_NAME \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 12 \
  --learning_rate 3e-5 \
  --num_train_epochs 2 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir ./tmp/result/ ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0 ### Training results

* train metrics *
  epoch                    =        2.0
  train_loss               =     0.9724
  train_runtime            = 2:57:05.43
  train_samples            =     148108
  train_samples_per_second =     27.878
  train_steps_per_second   =      2.323 ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
# result

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.## Model description

The model was trained with the NaturalQuestions dataset from URL## Intended uses & limitations

This fine-tuned model is used in the context of the NLP4Web course at TU Darmstadt## Training and evaluation data

The NaturalQuestions dataset from URL was used## Training procedure

The pytorch question-answering/run_qa.py script was used to fine tune the model.
The following parameters were passed to the script 
  --model_name_or_path distilbert-base-uncased \
  --train_file $TRAIN_FILE_NAME \
  --validation_file $EVAL_FILE_NAME \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 12 \
  --learning_rate 3e-5 \
  --num_train_epochs 2 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir ./tmp/result/### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0### Training results

* train metrics *
  epoch                    =        2.0
  train_loss               =     0.9724
  train_runtime            = 2:57:05.43
  train_samples            =     148108
  train_samples_per_second =     27.878
  train_steps_per_second   =      2.323### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2