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
+- rouge
+model-index:
+- name: 3d2804b2ecbaf90df95e1b8ae09af2bf
+  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. -->
+
+# 3d2804b2ecbaf90df95e1b8ae09af2bf
+
+This model is a fine-tuned version of [albert/albert-large-v1](https://huggingface.co/albert/albert-large-v1) on the dim/tldr_news dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9428
+- Data Size: 1.0
+- Epoch Runtime: 11.7767
+- Accuracy: 0.7812
+- F1 Macro: 0.8168
+- Rouge1: 0.7812
+- Rouge2: 0.0
+- Rougel: 0.7812
+- Rougelsum: 0.7812
+
+## 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 | Rouge1 | Rouge2 | Rougel | Rougelsum |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------------:|:--------:|:--------:|:------:|:------:|:------:|:---------:|
+| No log        | 0     | 0    | 1.6745          | 0         | 1.3944        | 0.2259   | 0.1392   | 0.2251 | 0.0    | 0.2259 | 0.2251    |
+| No log        | 1     | 178  | 1.3931          | 0.0078    | 1.6528        | 0.4276   | 0.2479   | 0.4283 | 0.0    | 0.4268 | 0.4276    |
+| No log        | 2     | 356  | 1.1945          | 0.0156    | 1.5635        | 0.4844   | 0.3175   | 0.4858 | 0.0    | 0.4844 | 0.4844    |
+| No log        | 3     | 534  | 1.0478          | 0.0312    | 1.7424        | 0.5973   | 0.4862   | 0.5980 | 0.0    | 0.5987 | 0.5987    |
+| No log        | 4     | 712  | 0.8938          | 0.0625    | 2.1720        | 0.6349   | 0.4960   | 0.6349 | 0.0    | 0.6357 | 0.6342    |
+| No log        | 5     | 890  | 0.8092          | 0.125     | 2.8107        | 0.6946   | 0.5266   | 0.6953 | 0.0    | 0.6946 | 0.6953    |
+| 0.0603        | 6     | 1068 | 0.7268          | 0.25      | 4.0413        | 0.7138   | 0.5772   | 0.7138 | 0.0    | 0.7138 | 0.7138    |
+| 0.6491        | 7     | 1246 | 0.6573          | 0.5       | 6.5657        | 0.7408   | 0.7500   | 0.7415 | 0.0    | 0.7415 | 0.7408    |
+| 0.5413        | 8.0   | 1424 | 0.6875          | 1.0       | 11.7105       | 0.7337   | 0.7345   | 0.7351 | 0.0    | 0.7344 | 0.7337    |
+| 0.4407        | 9.0   | 1602 | 0.6450          | 1.0       | 11.5885       | 0.7656   | 0.7938   | 0.7656 | 0.0    | 0.7656 | 0.7663    |
+| 0.3626        | 10.0  | 1780 | 0.6046          | 1.0       | 11.3687       | 0.7727   | 0.8022   | 0.7734 | 0.0    | 0.7727 | 0.7734    |
+| 0.2794        | 11.0  | 1958 | 0.7649          | 1.0       | 11.7194       | 0.7472   | 0.7900   | 0.7479 | 0.0    | 0.7472 | 0.7472    |
+| 0.2268        | 12.0  | 2136 | 0.9018          | 1.0       | 11.7140       | 0.7670   | 0.8029   | 0.7678 | 0.0    | 0.7667 | 0.7678    |
+| 0.1738        | 13.0  | 2314 | 0.9551          | 1.0       | 11.6256       | 0.75     | 0.7936   | 0.75   | 0.0    | 0.75   | 0.7507    |
+| 0.1162        | 14.0  | 2492 | 0.9428          | 1.0       | 11.7767       | 0.7812   | 0.8168   | 0.7812 | 0.0    | 0.7812 | 0.7812    |
+
+
+### Framework versions
+
+- Transformers 4.57.0
+- Pytorch 2.8.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.22.1

config.json

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+{
+  "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,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4"
+  },
+  "initializer_range": 0.02,
+  "inner_group_num": 1,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4
+  },
+  "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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+{
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+  "cls_token": "[CLS]",
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+  "mask_token": {
+    "content": "[MASK]",
+    "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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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "rstrip": false,
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[CLS]",
+  "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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