End of training

README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model: distilbert-base-cased
+tags:
+- pytorch
+- movie-review-sentiment
+- BertForSequenceClassification
+- generated_from_trainer
+metrics:
+- accuracy
+- matthews_correlation
+model-index:
+- name: distilbert-base-imdb
+  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. -->
+
+# distilbert-base-imdb
+
+This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the imdb dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3490
+- Accuracy: 0.9315
+- Matthews Correlation: 0.8630
+
+## 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: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 320
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Matthews Correlation |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------------:|
+| 0.2597        | 1.0   | 1250 | 0.1997          | 0.921    | 0.8426               |
+| 0.165         | 2.0   | 2500 | 0.1839          | 0.9291   | 0.8582               |
+| 0.0788        | 3.0   | 3750 | 0.2218          | 0.9308   | 0.8617               |
+| 0.0235        | 4.0   | 5000 | 0.3490          | 0.9315   | 0.8630               |
+| 0.0123        | 5.0   | 6250 | 0.3721          | 0.9314   | 0.8628               |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

config.json

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+{
+  "_name_or_path": "distilbert-base-cased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "NEGATIVE",
+    "1": "POSITIVE"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "NEGATIVE": 0,
+    "POSITIVE": 1
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "vocab_size": 28996
+}

evaluate_results.json

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+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.9315,
+    "eval_loss": 0.34898611903190613,
+    "eval_matthews_correlation": 0.8630000566256675,
+    "eval_runtime": 51.1853,
+    "eval_samples_per_second": 195.369,
+    "eval_steps_per_second": 0.625
+}

model.safetensors

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runs/Dec01_17-27-06_51337dcda21a/events.out.tfevents.1701451630.51337dcda21a.10843.0

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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
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+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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