End of training

README.md

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+---
+license: apache-2.0
+base_model: studio-ousia/luke-japanese-base-lite
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: out
+  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. -->
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/rspeech3399/huggingface/runs/a23y11m9)
+# out
+
+This model is a fine-tuned version of [studio-ousia/luke-japanese-base-lite](https://huggingface.co/studio-ousia/luke-japanese-base-lite) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2009
+- Accuracy: 0.9283
+- Precision: 0.9198
+- Recall: 0.9384
+- F1: 0.9290
+
+## 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: 32
+- eval_batch_size: 32
+- 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 | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.316         | 1.0   | 1479 | 0.2245          | 0.9127   | 0.9027    | 0.9251 | 0.9138 |
+| 0.1696        | 2.0   | 2958 | 0.1869          | 0.9308   | 0.9234    | 0.9395 | 0.9314 |
+| 0.1427        | 3.0   | 4437 | 0.2009          | 0.9283   | 0.9198    | 0.9384 | 0.9290 |
+
+
+### Framework versions
+
+- Transformers 4.42.3
+- Pytorch 2.1.0+cu118
+- Datasets 2.20.0
+- Tokenizers 0.19.1

added_tokens.json

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+{
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+  "<ent>": 32770
+}

config.json

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+{
+  "_name_or_path": "studio-ousia/luke-japanese-base-lite",
+  "architectures": [
+    "LukeForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bert_model_name": "models/luke-japanese/hf_xlm_roberta",
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "cls_entity_prediction": false,
+  "entity_emb_size": 256,
+  "entity_vocab_size": 4,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "luke",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.42.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "use_entity_aware_attention": true,
+  "vocab_size": 32772
+}

entity_vocab.json

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+{
+  "[MASK2]": 3,
+  "[MASK]": 0,
+  "[PAD]": 2,
+  "[UNK]": 1
+}

model.safetensors

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sentencepiece.bpe.model

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special_tokens_map.json

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tokenizer_config.json

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+  "sp_model_kwargs": {},
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+  "tokenizer_class": "MLukeTokenizer",
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+}

training_args.bin

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