End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: albert/albert-xxlarge-v2
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- rouge
+model-index:
+- name: 9984b730e8070704466d394c09272b8a
+  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. -->
+
+# 9984b730e8070704466d394c09272b8a
+
+This model is a fine-tuned version of [albert/albert-xxlarge-v2](https://huggingface.co/albert/albert-xxlarge-v2) on the nyu-mll/glue dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5860
+- Data Size: 1.0
+- Epoch Runtime: 17.2628
+- Accuracy: 0.8086
+- F1 Macro: 0.7769
+- Rouge1: 0.8096
+- Rouge2: 0.0
+- Rougel: 0.8086
+- Rougelsum: 0.8086
+
+## 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    | 0.6812          | 0         | 1.0866        | 0.6377   | 0.5163   | 0.6377 | 0.0    | 0.6377 | 0.6387    |
+| No log        | 1     | 267  | 0.6836          | 0.0078    | 1.7720        | 0.5205   | 0.4498   | 0.5205 | 0.0    | 0.5205 | 0.5205    |
+| No log        | 2     | 534  | 0.6158          | 0.0156    | 1.7797        | 0.6885   | 0.4108   | 0.6895 | 0.0    | 0.6885 | 0.6885    |
+| No log        | 3     | 801  | 0.6296          | 0.0312    | 2.1033        | 0.6611   | 0.6026   | 0.6611 | 0.0    | 0.6611 | 0.6616    |
+| No log        | 4     | 1068 | 0.5519          | 0.0625    | 2.4921        | 0.7021   | 0.4733   | 0.7031 | 0.0    | 0.7031 | 0.7012    |
+| 0.0336        | 5     | 1335 | 0.6704          | 0.125     | 3.4358        | 0.7236   | 0.5739   | 0.7236 | 0.0    | 0.7236 | 0.7231    |
+| 0.4919        | 6     | 1602 | 0.4829          | 0.25      | 5.3458        | 0.7871   | 0.7350   | 0.7861 | 0.0    | 0.7861 | 0.7871    |
+| 0.4105        | 7     | 1869 | 0.4351          | 0.5       | 9.3113        | 0.8174   | 0.7753   | 0.8174 | 0.0    | 0.8174 | 0.8174    |
+| 0.3572        | 8.0   | 2136 | 0.4500          | 1.0       | 17.4779       | 0.8164   | 0.7780   | 0.8164 | 0.0    | 0.8164 | 0.8164    |
+| 0.2837        | 9.0   | 2403 | 0.4056          | 1.0       | 17.1662       | 0.8320   | 0.7872   | 0.8320 | 0.0    | 0.8320 | 0.8320    |
+| 0.2434        | 10.0  | 2670 | 0.4391          | 1.0       | 17.1180       | 0.8320   | 0.7996   | 0.8320 | 0.0    | 0.8320 | 0.8320    |
+| 0.1855        | 11.0  | 2937 | 0.4628          | 1.0       | 17.0875       | 0.8232   | 0.7965   | 0.8232 | 0.0    | 0.8232 | 0.8223    |
+| 0.1922        | 12.0  | 3204 | 0.5114          | 1.0       | 17.1420       | 0.8057   | 0.7759   | 0.8057 | 0.0    | 0.8047 | 0.8066    |
+| 0.1294        | 13.0  | 3471 | 0.5860          | 1.0       | 17.2628       | 0.8086   | 0.7769   | 0.8096 | 0.0    | 0.8086 | 0.8086    |
+
+
+### 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,
+  "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_new",
+  "hidden_dropout_prob": 0,
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "inner_group_num": 1,
+  "intermediate_size": 16384,
+  "layer_norm_eps": 1e-12,
+  "layers_to_keep": [],
+  "max_position_embeddings": 512,
+  "model_type": "albert",
+  "net_structure_type": 0,
+  "num_attention_heads": 64,
+  "num_hidden_groups": 1,
+  "num_hidden_layers": 12,
+  "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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+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "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": {
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+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "special": true
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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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