
# bert-base-uncased-squad-v1-jpqd-ov-int8

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset.
It was compressed using [NNCF](https://github.com/openvinotoolkit/nncf) with [Optimum
Intel](https://github.com/huggingface/optimum-intel#openvino) following the [JPQD question-answering
example](https://github.com/huggingface/optimum-intel/tree/main/examples/openvino/question-answering#joint-pruning-quantization-and-distillation-jpqd-for-bert-on-squad10).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8.0
- mixed_precision_training: Native AMP


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2


{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-squad-v1-jpqd-ov-int8", "results": []}]}
transformers openvino bert generated_from_trainer dataset:squad license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #openvino #bert #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #has_space #region-us 


# bert-base-uncased-squad-v1-jpqd-ov-int8

This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It was compressed using NNCF with Optimum
Intel following the JPQD question-answering
example.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8.0
- mixed_precision_training: Native AMP


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2


# bert-base-uncased-squad-v1-jpqd-ov-int8

This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It was compressed using NNCF with Optimum
Intel following the JPQD question-answering
example. ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8.0
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #openvino #bert #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # bert-base-uncased-squad-v1-jpqd-ov-int8

This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It was compressed using NNCF with Optimum
Intel following the JPQD question-answering
example. ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8.0
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #openvino #bert #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #has_space #region-us 
# bert-base-uncased-squad-v1-jpqd-ov-int8

This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It was compressed using NNCF with Optimum
Intel following the JPQD question-answering
example.### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8.0
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2