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

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OpenVINO
bert
generated_from_trainer
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  1. README.md +49 -0
  2. all_results.json +11 -0
  3. config.json +26 -0
  4. openvino_config.json +99 -0
  5. openvino_model.bin +3 -0
  6. openvino_model.xml +0 -0
  7. special_tokens_map.json +7 -0
  8. structured_sparsity.csv +73 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +14 -0
  11. training_args.bin +3 -0
  12. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - squad
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+ model-index:
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+ - name: bert-base-uncased-squad-v1-jpqd-ov-int8
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # bert-base-uncased-squad-v1-jpqd-ov-int8
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+
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+ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset.
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+ It was compressed using [NNCF](https://github.com/openvinotoolkit/nncf) with [Optimum
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+ Intel](https://github.com/huggingface/optimum-intel#openvino) following the [JPQD question-answering
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+ example](https://github.com/huggingface/optimum-intel/tree/main/examples/openvino/question-answering#joint-pruning-quantization-and-distillation-jpqd-for-bert-on-squad10).
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 128
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 8.0
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ ```
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+ ***** eval metrics *****
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+ epoch = 8.0
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+ eval_exact_match = 83.141
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+ eval_f1 = 89.5906
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+ eval_samples = 10784
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+ ```
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.1
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+ - Pytorch 1.13.1+cu117
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+ - Datasets 2.8.0
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+ - Tokenizers 0.13.2
all_results.json ADDED
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+ {
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+ "epoch": 8.0,
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+ "eval_exact_match": 83.14096499526963,
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+ "eval_f1": 89.59061048191492,
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+ "eval_samples": 10784,
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+ "train_loss": 2.368001897127539,
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+ "train_runtime": 49077.3955,
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+ "train_samples": 88524,
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+ "train_samples_per_second": 14.43,
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+ "train_steps_per_second": 0.902
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+ }
config.json ADDED
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+ {
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+ "_name_or_path": "bert-base-uncased",
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+ "architectures": [
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+ "NNCFNetwork"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.26.1",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 30522
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+ }
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+ ],
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+ "params": {
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+ "enable_structured_masking": true,
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+ "importance_regularization_factor": 0.02,
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+ "warmup_end_epoch": 4,
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+ "algorithm": "quantization",
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+ "export_to_onnx_standard_ops": false,
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+ "input_info": [
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+ "keyword": "input_ids",
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+ "type": "long"
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+ }
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+ ],
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+ "optimum_version": "1.6.4",
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+ "save_onnx_model": false,
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+ "transformers_version": "4.26.1"
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+ }
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45
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46
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47
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48
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49
+ 47,15,FF,nncf_module.bert.encoder.layer.7.output.dense,"(768, 3072)","(768, 1858)","(768,)","(768,)",[1858 items],BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/ModuleList[layer]/BertLayer[7]/BertOutput[output]/NNCFLinear[dense]/linear_0
50
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51
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52
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53
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54
+ 52,17,FF,nncf_module.bert.encoder.layer.8.intermediate.dense,"(3072, 768)","(1637, 768)","(3072,)","(1637,)",[1637 items],BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/ModuleList[layer]/BertLayer[8]/BertIntermediate[intermediate]/NNCFLinear[dense]/linear_0
55
+ 53,17,FF,nncf_module.bert.encoder.layer.8.output.dense,"(768, 3072)","(768, 1637)","(768,)","(768,)",[1637 items],BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/ModuleList[layer]/BertLayer[8]/BertOutput[output]/NNCFLinear[dense]/linear_0
56
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57
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58
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59
+ 57,18,MHSA,nncf_module.bert.encoder.layer.9.attention.output.dense,"(768, 768)","(768, 448)","(768,)","(768,)","[0, 2, 3, 6, 8, 9, 11]",BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/ModuleList[layer]/BertLayer[9]/BertAttention[attention]/BertSelfOutput[output]/NNCFLinear[dense]/linear_0
60
+ 58,19,FF,nncf_module.bert.encoder.layer.9.intermediate.dense,"(3072, 768)","(1257, 768)","(3072,)","(1257,)",[1257 items],BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/ModuleList[layer]/BertLayer[9]/BertIntermediate[intermediate]/NNCFLinear[dense]/linear_0
61
+ 59,19,FF,nncf_module.bert.encoder.layer.9.output.dense,"(768, 3072)","(768, 1257)","(768,)","(768,)",[1257 items],BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/ModuleList[layer]/BertLayer[9]/BertOutput[output]/NNCFLinear[dense]/linear_0
62
+ 60,20,MHSA,nncf_module.bert.encoder.layer.10.attention.self.query,"(768, 768)","(384, 768)","(768,)","(384,)","[0, 2, 3, 9, 10, 11]",BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/ModuleList[layer]/BertLayer[10]/BertAttention[attention]/BertSelfAttention[self]/NNCFLinear[query]/linear_0
63
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64
+ 62,20,MHSA,nncf_module.bert.encoder.layer.10.attention.self.value,"(768, 768)","(384, 768)","(768,)","(384,)","[0, 2, 3, 9, 10, 11]",BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/ModuleList[layer]/BertLayer[10]/BertAttention[attention]/BertSelfAttention[self]/NNCFLinear[value]/linear_0
65
+ 63,20,MHSA,nncf_module.bert.encoder.layer.10.attention.output.dense,"(768, 768)","(768, 384)","(768,)","(768,)","[0, 2, 3, 9, 10, 11]",BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/ModuleList[layer]/BertLayer[10]/BertAttention[attention]/BertSelfOutput[output]/NNCFLinear[dense]/linear_0
66
+ 64,21,FF,nncf_module.bert.encoder.layer.10.intermediate.dense,"(3072, 768)","(1159, 768)","(3072,)","(1159,)",[1159 items],BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/ModuleList[layer]/BertLayer[10]/BertIntermediate[intermediate]/NNCFLinear[dense]/linear_0
67
+ 65,21,FF,nncf_module.bert.encoder.layer.10.output.dense,"(768, 3072)","(768, 1159)","(768,)","(768,)",[1159 items],BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/ModuleList[layer]/BertLayer[10]/BertOutput[output]/NNCFLinear[dense]/linear_0
68
+ 66,22,MHSA,nncf_module.bert.encoder.layer.11.attention.self.query,"(768, 768)","(384, 768)","(768,)","(384,)","[0, 1, 2, 4, 6, 9]",BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/ModuleList[layer]/BertLayer[11]/BertAttention[attention]/BertSelfAttention[self]/NNCFLinear[query]/linear_0
69
+ 67,22,MHSA,nncf_module.bert.encoder.layer.11.attention.self.key,"(768, 768)","(384, 768)","(768,)","(384,)","[0, 1, 2, 4, 6, 9]",BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/ModuleList[layer]/BertLayer[11]/BertAttention[attention]/BertSelfAttention[self]/NNCFLinear[key]/linear_0
70
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71
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72
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73
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vocab.txt ADDED
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