End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: xlnet/xlnet-base-cased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+model-index:
+- name: xlnet-fine-tuned-matching
+  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. -->
+
+# xlnet-fine-tuned-matching
+
+This model is a fine-tuned version of [xlnet/xlnet-base-cased](https://huggingface.co/xlnet/xlnet-base-cased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3020
+- Precision: 0.8308
+- Recall: 0.6207
+- F1: 0.7105
+
+## 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: 1e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
+| 0.404         | 1.0   | 185  | 0.2996          | 0.6829    | 0.3218 | 0.4375 |
+| 0.2827        | 2.0   | 370  | 0.2278          | 0.8144    | 0.6054 | 0.6945 |
+| 0.2288        | 3.0   | 555  | 0.2090          | 0.815     | 0.6245 | 0.7072 |
+| 0.1982        | 4.0   | 740  | 0.2250          | 0.7920    | 0.6858 | 0.7351 |
+| 0.18          | 5.0   | 925  | 0.2729          | 0.8812    | 0.5402 | 0.6698 |
+| 0.1703        | 6.0   | 1110 | 0.3118          | 0.8867    | 0.5096 | 0.6472 |
+| 0.1604        | 7.0   | 1295 | 0.2985          | 0.8696    | 0.6130 | 0.7191 |
+| 0.1522        | 8.0   | 1480 | 0.2928          | 0.8402    | 0.6245 | 0.7165 |
+| 0.149         | 9.0   | 1665 | 0.2910          | 0.8168    | 0.6322 | 0.7127 |
+| 0.1375        | 10.0  | 1850 | 0.3020          | 0.8308    | 0.6207 | 0.7105 |
+
+
+### Framework versions
+
+- Transformers 4.52.4
+- Pytorch 2.6.0+cu124
+- Datasets 2.14.4
+- Tokenizers 0.21.1

config.json

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+{
+  "architectures": [
+    "XLNetForSequenceClassification"
+  ],
+  "attn_type": "bi",
+  "bi_data": false,
+  "bos_token_id": 1,
+  "clamp_len": -1,
+  "d_head": 64,
+  "d_inner": 3072,
+  "d_model": 768,
+  "dropout": 0.1,
+  "end_n_top": 5,
+  "eos_token_id": 2,
+  "ff_activation": "gelu",
+  "initializer_range": 0.02,
+  "layer_norm_eps": 1e-12,
+  "mem_len": null,
+  "model_type": "xlnet",
+  "n_head": 12,
+  "n_layer": 12,
+  "pad_token_id": 5,
+  "problem_type": "single_label_classification",
+  "reuse_len": null,
+  "same_length": false,
+  "start_n_top": 5,
+  "summary_activation": "tanh",
+  "summary_last_dropout": 0.1,
+  "summary_type": "last",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 250
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
+  "untie_r": true,
+  "use_mems_eval": true,
+  "use_mems_train": false,
+  "vocab_size": 32000
+}

model.safetensors

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special_tokens_map.json

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+}

spiece.model

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tokenizer_config.json

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+  "extra_special_tokens": {},
+  "keep_accents": false,
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "remove_space": true,
+  "sep_token": "<sep>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLNetTokenizer",
+  "unk_token": "<unk>"
+}

training_args.bin

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