upload mrpc fine-tuned model

README.md

@@ -1,3 +1,77 @@
 ---
+language:
+- en
 license: mit
+tags:
+- generated_from_trainer
+datasets:
+- glue
+metrics:
+- accuracy
+- f1
+model-index:
+- name: MiniLM-L12-H384-uncased-mrpc
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: GLUE MRPC
+      type: glue
+      args: mrpc
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.875
+    - name: F1
+      type: f1
+      value: 0.9097345132743363
 ---
+
+<!-- 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. -->
+
+# MiniLM-L12-H384-uncased-mrpc
+
+This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the GLUE MRPC dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4319
+- Accuracy: 0.875
+- F1: 0.9097
+- Combined Score: 0.8924
+
+## 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 4.18.0
+- Pytorch 1.11.0+cu102
+- Datasets 2.2.2
+- Tokenizers 0.12.1

all_results.json

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+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.875,
+    "eval_combined_score": 0.8923672566371681,
+    "eval_f1": 0.9097345132743363,
+    "eval_loss": 0.43185245990753174,
+    "eval_runtime": 3.0165,
+    "eval_samples": 408,
+    "eval_samples_per_second": 135.255,
+    "eval_steps_per_second": 16.907,
+    "train_loss": 0.29514418560525646,
+    "train_runtime": 616.8399,
+    "train_samples": 3668,
+    "train_samples_per_second": 29.732,
+    "train_steps_per_second": 1.864
+}

config.json

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+{
+  "_name_or_path": "microsoft/MiniLM-L12-H384-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "mrpc",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "not_equivalent",
+    "1": "equivalent"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "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": "4.18.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

eval_results.json

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+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.875,
+    "eval_combined_score": 0.8923672566371681,
+    "eval_f1": 0.9097345132743363,
+    "eval_loss": 0.43185245990753174,
+    "eval_runtime": 3.0165,
+    "eval_samples": 408,
+    "eval_samples_per_second": 135.255,
+    "eval_steps_per_second": 16.907
+}

pytorch_model.bin

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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, "special_tokens_map_file": "/home2/xinhe/.cache/huggingface/transformers/1e5909e4dfaa904617797ed35a6105a23daa56cbefca48fef329f772584699fb.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "name_or_path": "microsoft/MiniLM-L12-H384-uncased", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"}

train_results.json

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+{
+    "epoch": 5.0,
+    "train_loss": 0.29514418560525646,
+    "train_runtime": 616.8399,
+    "train_samples": 3668,
+    "train_samples_per_second": 29.732,
+    "train_steps_per_second": 1.864
+}

trainer_state.json

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+{
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+  "best_model_checkpoint": null,
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+  "global_step": 1150,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
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+      "learning_rate": 1.1304347826086957e-05,
+      "loss": 0.4222,
+      "step": 500
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+    {
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+      "learning_rate": 2.6086956521739132e-06,
+      "loss": 0.2095,
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+      "train_steps_per_second": 1.864
+    }
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+  "num_train_epochs": 5,
+  "total_flos": 302026467563520.0,
+  "trial_name": null,
+  "trial_params": null
+}

training_args.bin

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