Training complete

README.md

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+---
+library_name: transformers
+license: mit
+base_model: roberta-large
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: roberta-large-binary-classification
+  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. -->
+
+# roberta-large-binary-classification
+
+This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6983
+- Accuracy: 0.7580
+- F1 Macro: 0.7453
+- Precision Macro: 0.7498
+- Recall Macro: 0.7425
+- Auc: 0.7941
+
+## 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | Auc    |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:------:|
+| No log        | 1.0   | 79   | 0.6751          | 0.5955   | 0.3733   | 0.2978          | 0.5          | 0.6194 |
+| No log        | 2.0   | 158  | 0.6642          | 0.5955   | 0.3733   | 0.2978          | 0.5          | 0.6210 |
+| No log        | 3.0   | 237  | 0.5609          | 0.7102   | 0.6895   | 0.7003          | 0.6859       | 0.7701 |
+| No log        | 4.0   | 316  | 0.5676          | 0.7070   | 0.6907   | 0.6954          | 0.6883       | 0.7734 |
+| No log        | 5.0   | 395  | 0.6983          | 0.7580   | 0.7453   | 0.7498          | 0.7425       | 0.7941 |
+| No log        | 6.0   | 474  | 0.7766          | 0.7420   | 0.7319   | 0.7322          | 0.7316       | 0.7802 |
+| 0.4887        | 7.0   | 553  | 1.1879          | 0.7452   | 0.7266   | 0.7399          | 0.7217       | 0.7761 |
+| 0.4887        | 8.0   | 632  | 1.6676          | 0.7484   | 0.7242   | 0.7504          | 0.7180       | 0.7789 |
+| 0.4887        | 9.0   | 711  | 1.6440          | 0.7548   | 0.7364   | 0.7511          | 0.7310       | 0.7889 |
+| 0.4887        | 10.0  | 790  | 1.7092          | 0.7548   | 0.7364   | 0.7511          | 0.7310       | 0.7928 |
+
+
+### Framework versions
+
+- Transformers 4.57.1
+- Pytorch 2.8.0+cu126
+- Datasets 4.0.0
+- Tokenizers 0.22.1

config.json

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+{
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

model.safetensors

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+oid sha256:443df74619bd134e866996d772cdd208205bbcb8493aa420cadc7d0ea317791c
+size 1421495416

training_args.bin

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