codebert-TD

README.md

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+---
+library_name: transformers
+base_model: microsoft/codebert-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: codebert-td
+  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. -->
+
+# codebert-td
+
+This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4325
+- Accuracy: 0.9492
+- F1 Macro: 0.6372
+- F1 Weighted: 0.9487
+
+## 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: 64
+- eval_batch_size: 64
+- 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
+- lr_scheduler_warmup_steps: 0.1
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|
+| 0.3710        | 1.0   | 539  | 0.3667          | 0.9399   | 0.2785   | 0.9265      |
+| 0.3190        | 2.0   | 1078 | 0.3273          | 0.9450   | 0.3737   | 0.9394      |
+| 0.2832        | 3.0   | 1617 | 0.3055          | 0.9513   | 0.5054   | 0.9483      |
+| 0.2518        | 4.0   | 2156 | 0.3008          | 0.9529   | 0.6363   | 0.9515      |
+| 0.1736        | 5.0   | 2695 | 0.3219          | 0.9520   | 0.6821   | 0.9520      |
+| 0.1768        | 6.0   | 3234 | 0.3548          | 0.9520   | 0.6803   | 0.9518      |
+| 0.1445        | 7.0   | 3773 | 0.3569          | 0.9524   | 0.6808   | 0.9525      |
+| 0.1154        | 8.0   | 4312 | 0.3944          | 0.9517   | 0.6895   | 0.9522      |
+| 0.0974        | 9.0   | 4851 | 0.4116          | 0.9524   | 0.6966   | 0.9527      |
+| 0.1000        | 10.0  | 5390 | 0.4149          | 0.9531   | 0.6886   | 0.9532      |
+
+
+### Framework versions
+
+- Transformers 5.0.0
+- Pytorch 2.10.0+cu128
+- Datasets 4.8.3
+- Tokenizers 0.22.2

config.json

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+{
+  "add_cross_attention": false,
+  "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": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "problem_type": "single_label_classification",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "type_vocab_size": 1,
+  "use_cache": false,
+  "vocab_size": 50265
+}

model.safetensors

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training_args.bin

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