End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: ai4bharat/IndicBERTv2-MLM-only
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: indic-bert-v2-mlm-only-dra-tam-mal-aw-classification-lora-r12
+  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. -->
+
+# indic-bert-v2-mlm-only-dra-tam-mal-aw-classification-lora-r12
+
+This model is a fine-tuned version of [ai4bharat/IndicBERTv2-MLM-only](https://huggingface.co/ai4bharat/IndicBERTv2-MLM-only) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5211
+- Accuracy: 0.7539
+- F1: 0.7405
+
+## 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: 0.0001
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 6
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
+| 0.6914        | 0.2222 | 20   | 0.6860          | 0.5827   | 0.2928 |
+| 0.6859        | 0.4444 | 40   | 0.6863          | 0.5338   | 0.6567 |
+| 0.6855        | 0.6667 | 60   | 0.6765          | 0.6324   | 0.5248 |
+| 0.6765        | 0.8889 | 80   | 0.6769          | 0.5542   | 0.6638 |
+| 0.6689        | 1.1111 | 100  | 0.6595          | 0.6487   | 0.5174 |
+| 0.6583        | 1.3333 | 120  | 0.6462          | 0.7001   | 0.6599 |
+| 0.6456        | 1.5556 | 140  | 0.6297          | 0.6805   | 0.6942 |
+| 0.626         | 1.7778 | 160  | 0.6033          | 0.7017   | 0.6995 |
+| 0.6082        | 2.0    | 180  | 0.5898          | 0.7033   | 0.7065 |
+| 0.5779        | 2.2222 | 200  | 0.5683          | 0.7188   | 0.6917 |
+| 0.5886        | 2.4444 | 220  | 0.5554          | 0.7229   | 0.6909 |
+| 0.5909        | 2.6667 | 240  | 0.5488          | 0.7311   | 0.7170 |
+| 0.5607        | 2.8889 | 260  | 0.5435          | 0.7327   | 0.7244 |
+| 0.5611        | 3.1111 | 280  | 0.5403          | 0.7368   | 0.7169 |
+| 0.5375        | 3.3333 | 300  | 0.5375          | 0.7311   | 0.7140 |
+| 0.5563        | 3.5556 | 320  | 0.5377          | 0.7425   | 0.7308 |
+| 0.5562        | 3.7778 | 340  | 0.5340          | 0.7376   | 0.7130 |
+| 0.568         | 4.0    | 360  | 0.5320          | 0.7457   | 0.7455 |
+| 0.5598        | 4.2222 | 380  | 0.5265          | 0.7433   | 0.7185 |
+| 0.5372        | 4.4444 | 400  | 0.5241          | 0.7531   | 0.7452 |
+| 0.5366        | 4.6667 | 420  | 0.5344          | 0.7498   | 0.7542 |
+| 0.5526        | 4.8889 | 440  | 0.5224          | 0.7514   | 0.7355 |
+| 0.523         | 5.1111 | 460  | 0.5237          | 0.7482   | 0.7258 |
+| 0.5362        | 5.3333 | 480  | 0.5235          | 0.7482   | 0.7406 |
+| 0.5374        | 5.5556 | 500  | 0.5216          | 0.7514   | 0.7359 |
+| 0.5621        | 5.7778 | 520  | 0.5213          | 0.7506   | 0.7325 |
+| 0.5373        | 6.0    | 540  | 0.5211          | 0.7539   | 0.7405 |
+
+
+### Framework versions
+
+- Transformers 4.45.2
+- Pytorch 2.5.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.20.3

config.json

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+{
+  "_name_or_path": "microsoft/Multilingual-MiniLM-L12-H384",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

default/head_config.json

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+{
+  "config": {
+    "activation_function": "gelu",
+    "bias": true,
+    "embedding_size": 768,
+    "head_type": "masked_lm",
+    "label2id": null,
+    "layer_norm": true,
+    "layers": 2,
+    "shift_labels": false,
+    "vocab_size": 250000
+  },
+  "hidden_size": 768,
+  "model_class": "BertAdapterModel",
+  "model_name": "ai4bharat/IndicBERTv2-MLM-only",
+  "model_type": "bert",
+  "name": "default",
+  "version": "adapters.1.0.1"
+}

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tam_mal_ai_aw_classification_adapter/adapter_config.json

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+{
+  "config": {
+    "alpha": 8,
+    "architecture": "lora",
+    "attn_matrices": [
+      "q",
+      "v"
+    ],
+    "composition_mode": "add",
+    "dropout": 0.1,
+    "init_weights": "lora",
+    "intermediate_lora": false,
+    "leave_out": [],
+    "output_lora": false,
+    "r": 12,
+    "selfattn_lora": true,
+    "use_gating": false
+  },
+  "config_id": "a0c8452a4cfb970e",
+  "hidden_size": 768,
+  "model_class": "BertAdapterModel",
+  "model_name": "ai4bharat/IndicBERTv2-MLM-only",
+  "model_type": "bert",
+  "name": "tam_mal_ai_aw_classification_adapter",
+  "version": "adapters.1.0.1"
+}

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tam_mal_ai_aw_classification_head/head_config.json

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+{
+  "config": {
+    "activation_function": "ReLU",
+    "bias": true,
+    "dropout_prob": null,
+    "head_type": "classification",
+    "label2id": {
+      "Abusive": 1,
+      "Non-Abusive": 0
+    },
+    "layers": 2,
+    "num_labels": 2,
+    "use_pooler": false
+  },
+  "hidden_size": 768,
+  "model_class": "BertAdapterModel",
+  "model_name": "ai4bharat/IndicBERTv2-MLM-only",
+  "model_type": "bert",
+  "name": "tam_mal_ai_aw_classification_head",
+  "version": "adapters.1.0.1"
+}

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training_args.bin

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