upload fine-tuned model

README.md

@@ -1,3 +1,77 @@
----
-license: apache-2.0
----
+---
+language:
+- en
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- glue
+metrics:
+- accuracy
+- f1
+model-index:
+- name: bert-base-uncased-mrpc
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: GLUE MRPC
+      type: glue
+      args: mrpc
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8602941176470589
+    - name: F1
+      type: f1
+      value: 0.9042016806722689
+---
+
+<!-- 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-base-uncased-5-256-16
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6978
+- Accuracy: 0.8603
+- F1: 0.9042
+- Combined Score: 0.8822
+
+## 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: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.17.0
+- Pytorch 1.10.0+cu102
+- Datasets 1.14.0
+- Tokenizers 0.11.6

all_results.json

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+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.8602941176470589,
+    "eval_combined_score": 0.8822478991596638,
+    "eval_f1": 0.9042016806722689,
+    "eval_loss": 0.6977788209915161,
+    "eval_runtime": 9.6009,
+    "eval_samples": 408,
+    "eval_samples_per_second": 42.496,
+    "eval_steps_per_second": 5.312,
+    "train_loss": 0.27140990464583686,
+    "train_runtime": 1685.2933,
+    "train_samples": 3668,
+    "train_samples_per_second": 10.882,
+    "train_steps_per_second": 0.682
+}

config.json

@@ -0,0 +1,36 @@
+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "mrpc",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "not_equivalent",
+    "1": "equivalent"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "equivalent": 1,
+    "not_equivalent": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.17.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

eval_results.json

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+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.8602941176470589,
+    "eval_combined_score": 0.8822478991596638,
+    "eval_f1": 0.9042016806722689,
+    "eval_loss": 0.6977788209915161,
+    "eval_runtime": 9.6009,
+    "eval_samples": 408,
+    "eval_samples_per_second": 42.496,
+    "eval_steps_per_second": 5.312
+}

pytorch_model.bin

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+size 438017325

special_tokens_map.json

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

tokenizer_config.json

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+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "bert-base-uncased", "tokenizer_class": "BertTokenizer"}

train_results.json

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+{
+    "epoch": 5.0,
+    "train_loss": 0.27140990464583686,
+    "train_runtime": 1685.2933,
+    "train_samples": 3668,
+    "train_samples_per_second": 10.882,
+    "train_steps_per_second": 0.682
+}

trainer_state.json

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+{
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+  "best_model_checkpoint": null,
+  "epoch": 5.0,
+  "global_step": 1150,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 2.17,
+      "learning_rate": 1.1304347826086957e-05,
+      "loss": 0.4474,
+      "step": 500
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+    {
+      "epoch": 4.35,
+      "learning_rate": 2.6086956521739132e-06,
+      "loss": 0.1548,
+      "step": 1000
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+      "train_loss": 0.27140990464583686,
+      "train_runtime": 1685.2933,
+      "train_samples_per_second": 10.882,
+      "train_steps_per_second": 0.682
+    }
+  ],
+  "max_steps": 1150,
+  "num_train_epochs": 5,
+  "total_flos": 2412728377651200.0,
+  "trial_name": null,
+  "trial_params": null
+}

training_args.bin

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