bert-p-tuning

README.md

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+---
+license: cc-by-4.0
+base_model: deepset/bert-base-cased-squad2
+tags:
+- generated_from_trainer
+model-index:
+- name: bert-26
+  results: []
+---
+
+<!-- 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. -->
+
+# bert-26
+
+This model is a fine-tuned version of [deepset/bert-base-cased-squad2](https://huggingface.co/deepset/bert-base-cased-squad2) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 6.8756
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 7.1774        | 0.18  | 5    | 7.2847          |
+| 6.9676        | 0.36  | 10   | 7.1874          |
+| 6.9639        | 0.54  | 15   | 7.1658          |
+| 6.821         | 0.71  | 20   | 7.1371          |
+| 6.7658        | 0.89  | 25   | 7.1082          |
+| 6.7752        | 1.07  | 30   | 7.0762          |
+| 6.7502        | 1.25  | 35   | 7.0440          |
+| 6.7292        | 1.43  | 40   | 7.0080          |
+| 6.6645        | 1.61  | 45   | 6.9767          |
+| 6.6253        | 1.79  | 50   | 6.9528          |
+| 6.6661        | 1.96  | 55   | 6.9324          |
+| 6.6808        | 2.14  | 60   | 6.9133          |
+| 6.692         | 2.32  | 65   | 6.8985          |
+| 6.5486        | 2.5   | 70   | 6.8864          |
+| 6.5355        | 2.68  | 75   | 6.8789          |
+| 6.5888        | 2.86  | 80   | 6.8756          |
+
+
+### Framework versions
+
+- Transformers 4.34.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.14.1

adapter_config.json

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+{
+  "auto_mapping": null,
+  "base_model_name_or_path": "deepset/bert-base-cased-squad2",
+  "encoder_dropout": 0.0,
+  "encoder_hidden_size": 128,
+  "encoder_num_layers": 2,
+  "encoder_reparameterization_type": "MLP",
+  "inference_mode": true,
+  "num_attention_heads": 12,
+  "num_layers": 12,
+  "num_transformer_submodules": 1,
+  "num_virtual_tokens": 20,
+  "peft_type": "P_TUNING",
+  "revision": null,
+  "task_type": "QUESTION_ANS",
+  "token_dim": 768
+}

adapter_model.bin

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+size 68993

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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