Training in progress, epoch 1

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- text-classification
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: yt-title-grader
+  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. -->
+
+# yt-title-grader
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the yt_main_dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2751
+- Accuracy: 0.8945
+
+## 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: 16
+- 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 | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 0.2641        | 1.0   | 5725  | 0.2751          | 0.8945   |
+| 0.2244        | 2.0   | 11450 | 0.2852          | 0.9021   |
+| 0.1598        | 3.0   | 17175 | 0.3307          | 0.9007   |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 1.13.1
+- Datasets 2.14.4
+- Tokenizers 0.13.3

all_results.json

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+{
+    "epoch": 3.0,
+    "eval_accuracy": 0.8944646814580839,
+    "eval_loss": 0.27506107091903687,
+    "eval_runtime": 24.0277,
+    "eval_samples_per_second": 1633.821,
+    "eval_steps_per_second": 102.132
+}

config.json

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+{
+  "_name_or_path": "distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "Low engagement",
+    "1": "High engagement"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "High engagement": 1,
+    "Low engagement": 0
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "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": "4.32.1",
+  "vocab_size": 30522
+}

eval_results.json

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+{
+    "epoch": 3.0,
+    "eval_accuracy": 0.8944646814580839,
+    "eval_loss": 0.27506107091903687,
+    "eval_runtime": 24.0277,
+    "eval_samples_per_second": 1633.821,
+    "eval_steps_per_second": 102.132
+}

pytorch_model.bin

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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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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "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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