Trained on syssec-utd/segmentation-py313-pylingual-v3-tokenized using syssec-utd/py313-pylingual-v3-mlm

README.md

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+---
+library_name: transformers
+base_model: syssec-utd/py313-pylingual-v3-mlm
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: py313-pylingual-v3-segmenter
+  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. -->
+
+# py313-pylingual-v3-segmenter
+
+This model is a fine-tuned version of [syssec-utd/py313-pylingual-v3-mlm](https://huggingface.co/syssec-utd/py313-pylingual-v3-mlm) on the syssec-utd/segmentation-py313-pylingual-v3-tokenized dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0047
+- Precision: 0.9945
+- Recall: 0.9944
+- F1: 0.9945
+- Accuracy: 0.9983
+
+## 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: 28
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- 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: 2
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step   | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0073        | 1.0   | 99547  | 0.0049          | 0.9939    | 0.9938 | 0.9939 | 0.9981   |
+| 0.0036        | 2.0   | 199094 | 0.0047          | 0.9945    | 0.9944 | 0.9945 | 0.9983   |
+
+
+### Framework versions
+
+- Transformers 5.12.1
+- Pytorch 2.12.0+cu130
+- Datasets 5.0.0
+- Tokenizers 0.22.2

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "RobertaForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "B",
+    "1": "I",
+    "2": "E"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "label2id": {
+    "B": "0",
+    "E": "2",
+    "I": "1"
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 1026,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.12.1",
+  "type_vocab_size": 1,
+  "use_cache": false,
+  "vocab_size": 30000
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
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+size 435572084

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "[UNK]"
+}

training_args.bin

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+size 5265