End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: albert/albert-large-v1
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: d80397db0ea0d34eec1577aaae2d31cd
+  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. -->
+
+# d80397db0ea0d34eec1577aaae2d31cd
+
+This model is a fine-tuned version of [albert/albert-large-v1](https://huggingface.co/albert/albert-large-v1) on the contemmcm/clickbait dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2128
+- Data Size: 1.0
+- Epoch Runtime: 40.4139
+- Accuracy: 0.9416
+- F1 Macro: 0.9368
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- total_train_batch_size: 32
+- total_eval_batch_size: 32
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: constant
+- num_epochs: 50
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Data Size | Epoch Runtime | Accuracy | F1 Macro |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------------:|:--------:|:--------:|
+| No log        | 0     | 0    | 0.7052          | 0         | 3.4264        | 0.4809   | 0.4313   |
+| No log        | 1     | 650  | 0.3530          | 0.0078    | 3.9987        | 0.8951   | 0.8852   |
+| No log        | 2     | 1300 | 0.0785          | 0.0156    | 4.0945        | 0.9776   | 0.9765   |
+| No log        | 3     | 1950 | 0.1048          | 0.0312    | 4.7470        | 0.9686   | 0.9672   |
+| No log        | 4     | 2600 | 0.1221          | 0.0625    | 5.7612        | 0.9510   | 0.9471   |
+| 0.0075        | 5     | 3250 | 0.0807          | 0.125     | 8.2096        | 0.9728   | 0.9710   |
+| 0.0501        | 6     | 3900 | 0.0544          | 0.25      | 12.6979       | 0.9865   | 0.9858   |
+| 0.0666        | 7     | 4550 | 0.0446          | 0.5       | 22.1560       | 0.9904   | 0.9898   |
+| 0.6766        | 8.0   | 5200 | 0.6740          | 1.0       | 41.1326       | 0.6130   | 0.3801   |
+| 0.6684        | 9.0   | 5850 | 0.6729          | 1.0       | 40.1185       | 0.6130   | 0.3801   |
+| 0.5126        | 10.0  | 6500 | 0.4935          | 1.0       | 40.8441       | 0.7743   | 0.7167   |
+| 0.2079        | 11.0  | 7150 | 0.2128          | 1.0       | 40.4139       | 0.9416   | 0.9368   |
+
+
+### Framework versions
+
+- Transformers 4.57.0
+- Pytorch 2.8.0+cu128
+- Datasets 4.3.0
+- Tokenizers 0.22.1

config.json

@@ -0,0 +1,33 @@
+{
+  "architectures": [
+    "AlbertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 2,
+  "classifier_dropout_prob": 0.1,
+  "down_scale_factor": 1,
+  "dtype": "float32",
+  "embedding_size": 128,
+  "eos_token_id": 3,
+  "gap_size": 0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "inner_group_num": 1,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "albert",
+  "net_structure_type": 0,
+  "num_attention_heads": 16,
+  "num_hidden_groups": 1,
+  "num_hidden_layers": 24,
+  "num_memory_blocks": 0,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.0",
+  "type_vocab_size": 2,
+  "vocab_size": 30000
+}

model.safetensors

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special_tokens_map.json

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+  "mask_token": {
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+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "[SEP]",
+  "unk_token": "<unk>"
+}

spiece.model

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tokenizer_config.json

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+{
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+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "eos_token": "[SEP]",
+  "extra_special_tokens": {},
+  "keep_accents": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "remove_space": true,
+  "sep_token": "[SEP]",
+  "tokenizer_class": "AlbertTokenizer",
+  "unk_token": "<unk>"
+}

training_args.bin

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