End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: albert/albert-xlarge-v2
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: 88baaa0fc0d014bbfbf8077c9420beea
+  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. -->
+
+# 88baaa0fc0d014bbfbf8077c9420beea
+
+This model is a fine-tuned version of [albert/albert-xlarge-v2](https://huggingface.co/albert/albert-xlarge-v2) on the ccdv/patent-classification [abstract] dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.9926
+- Data Size: 1.0
+- Epoch Runtime: 145.0872
+- Accuracy: 0.2218
+- F1 Macro: 0.0403
+
+## 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    | 2.4026          | 0         | 9.6523        | 0.1004   | 0.0203   |
+| No log        | 1     | 781  | 2.0320          | 0.0078    | 11.3637       | 0.1538   | 0.0372   |
+| No log        | 2     | 1562 | 2.0092          | 0.0156    | 11.9116       | 0.1510   | 0.0292   |
+| No log        | 3     | 2343 | 2.0049          | 0.0312    | 14.1118       | 0.2071   | 0.0381   |
+| 0.0454        | 4     | 3124 | 2.0001          | 0.0625    | 18.2314       | 0.2218   | 0.0403   |
+| 2.0154        | 5     | 3905 | 1.9943          | 0.125     | 26.8018       | 0.2218   | 0.0403   |
+| 2.0269        | 6     | 4686 | 1.9923          | 0.25      | 43.9274       | 0.2071   | 0.0381   |
+| 1.9998        | 7     | 5467 | 1.9855          | 0.5       | 77.6442       | 0.2071   | 0.0381   |
+| 1.9763        | 8.0   | 6248 | 1.9884          | 1.0       | 145.9947      | 0.2161   | 0.0648   |
+| 1.972         | 9.0   | 7029 | 1.9885          | 1.0       | 145.3029      | 0.2071   | 0.0381   |
+| 1.985         | 10.0  | 7810 | 1.9864          | 1.0       | 145.0537      | 0.2218   | 0.0403   |
+| 1.9704        | 11.0  | 8591 | 1.9926          | 1.0       | 145.0872      | 0.2218   | 0.0403   |
+
+
+### Framework versions
+
+- Transformers 4.57.0
+- Pytorch 2.8.0+cu128
+- Datasets 4.3.0
+- 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": 2048,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8"
+  },
+  "initializer_range": 0.02,
+  "inner_group_num": 1,
+  "intermediate_size": 8192,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8
+  },
+  "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]",
+  "eos_token": "[SEP]",
+  "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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+{
+  "added_tokens_decoder": {
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+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+  "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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