Model save

README.md

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+---
+library_name: transformers
+license: mit
+base_model: FacebookAI/xlm-roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- accuracy
+model-index:
+- name: gemma-fineweb-edu-scorer-xlm-binary-xlm-roberta-base-lr5e-05-20250411_122512
+  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. -->
+
+# gemma-fineweb-edu-scorer-xlm-binary-xlm-roberta-base-lr5e-05-20250411_122512
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1484
+- Precision: 0.7851
+- Recall: 0.7983
+- F1 Macro: 0.7913
+- Accuracy: 0.8438
+
+## 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: 64
+- eval_batch_size: 128
+- seed: 0
+- 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: 20
+
+### Training results
+
+| Training Loss | Epoch   | Step  | Validation Loss | Precision | Recall | F1 Macro | Accuracy |
+|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|
+| No log        | 0       | 0     | 0.2350          | 0.3796    | 0.5    | 0.4316   | 0.7593   |
+| 0.1059        | 0.7812  | 1000  | 0.1000          | 0.8077    | 0.7659 | 0.7830   | 0.8517   |
+| 0.0825        | 1.5625  | 2000  | 0.0995          | 0.8129    | 0.7662 | 0.7850   | 0.8540   |
+| 0.0563        | 2.3438  | 3000  | 0.1118          | 0.7910    | 0.8195 | 0.8031   | 0.8488   |
+| 0.0315        | 3.125   | 4000  | 0.1183          | 0.8049    | 0.7861 | 0.7947   | 0.8545   |
+| 0.0359        | 3.9062  | 5000  | 0.1234          | 0.8174    | 0.7528 | 0.7766   | 0.8524   |
+| 0.0213        | 4.6875  | 6000  | 0.1431          | 0.7791    | 0.8259 | 0.7960   | 0.8374   |
+| 0.0162        | 5.4688  | 7000  | 0.1312          | 0.7904    | 0.8026 | 0.7961   | 0.8478   |
+| 0.0111        | 6.25    | 8000  | 0.1509          | 0.7817    | 0.8155 | 0.7955   | 0.8411   |
+| 0.013         | 7.0312  | 9000  | 0.1242          | 0.7998    | 0.7994 | 0.7996   | 0.8536   |
+| 0.0135        | 7.8125  | 10000 | 0.1292          | 0.8162    | 0.7570 | 0.7794   | 0.8530   |
+| 0.0094        | 8.5938  | 11000 | 0.1463          | 0.7814    | 0.8172 | 0.7957   | 0.8406   |
+| 0.0063        | 9.375   | 12000 | 0.1427          | 0.7799    | 0.8202 | 0.7954   | 0.8390   |
+| 0.0082        | 10.1562 | 13000 | 0.1369          | 0.7838    | 0.8082 | 0.7944   | 0.8430   |
+| 0.0081        | 10.9375 | 14000 | 0.1390          | 0.7880    | 0.7840 | 0.7859   | 0.8446   |
+| 0.0058        | 11.7188 | 15000 | 0.1421          | 0.7833    | 0.8072 | 0.7937   | 0.8426   |
+| 0.0061        | 12.5    | 16000 | 0.1580          | 0.7646    | 0.8190 | 0.7819   | 0.8225   |
+| 0.005         | 13.2812 | 17000 | 0.1414          | 0.7924    | 0.7963 | 0.7943   | 0.8486   |
+| 0.0031        | 14.0625 | 18000 | 0.1403          | 0.7935    | 0.8052 | 0.7990   | 0.8501   |
+| 0.0032        | 14.8438 | 19000 | 0.1412          | 0.7958    | 0.7873 | 0.7914   | 0.8496   |
+| 0.0046        | 15.625  | 20000 | 0.1458          | 0.7852    | 0.8119 | 0.7967   | 0.8441   |
+| 0.0027        | 16.4062 | 21000 | 0.1490          | 0.7832    | 0.8115 | 0.7952   | 0.8425   |
+| 0.0025        | 17.1875 | 22000 | 0.1442          | 0.7886    | 0.7995 | 0.7938   | 0.8463   |
+| 0.0025        | 17.9688 | 23000 | 0.1431          | 0.7962    | 0.7811 | 0.7881   | 0.8490   |
+| 0.0017        | 18.75   | 24000 | 0.1496          | 0.7834    | 0.8036 | 0.7925   | 0.8427   |
+| 0.0022        | 19.5312 | 25000 | 0.1484          | 0.7851    | 0.7983 | 0.7913   | 0.8438   |
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.1

final/config.json

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+  "hidden_act": "gelu",
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+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "regression",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

final/model.safetensors

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final/special_tokens_map.json

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final/tokenizer_config.json

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+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}

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