End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: microsoft/Multilingual-MiniLM-L12-H384
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: m-minilm-l12-h384-data-augumented-dra-tam-mal-aw-classification-finetune
+  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. -->
+
+# m-minilm-l12-h384-data-augumented-dra-tam-mal-aw-classification-finetune
+
+This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6411
+- Accuracy: 0.7702
+- F1: 0.8164
+
+## 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: 128
+- eval_batch_size: 128
+- 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     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
+| 0.6713        | 0.2222 | 20   | 0.6683          | 0.5998   | 0.7499 |
+| 0.6494        | 0.4444 | 40   | 0.6559          | 0.6019   | 0.6459 |
+| 0.6431        | 0.6667 | 60   | 0.6389          | 0.6508   | 0.7346 |
+| 0.6079        | 0.8889 | 80   | 0.6318          | 0.6720   | 0.7166 |
+| 0.5667        | 1.1111 | 100  | 0.5755          | 0.7021   | 0.7401 |
+| 0.5353        | 1.3333 | 120  | 0.5437          | 0.7213   | 0.7927 |
+| 0.5345        | 1.5556 | 140  | 0.5306          | 0.7482   | 0.7964 |
+| 0.5178        | 1.7778 | 160  | 0.5366          | 0.7184   | 0.8031 |
+| 0.4952        | 2.0    | 180  | 0.5046          | 0.7543   | 0.8050 |
+| 0.4183        | 2.2222 | 200  | 0.5798          | 0.7278   | 0.7466 |
+| 0.4257        | 2.4444 | 220  | 0.5373          | 0.7673   | 0.8075 |
+| 0.3932        | 2.6667 | 240  | 0.5214          | 0.7665   | 0.8093 |
+| 0.3914        | 2.8889 | 260  | 0.5125          | 0.7616   | 0.8133 |
+| 0.3447        | 3.1111 | 280  | 0.5534          | 0.7653   | 0.8076 |
+| 0.3122        | 3.3333 | 300  | 0.5874          | 0.7543   | 0.7901 |
+| 0.3116        | 3.5556 | 320  | 0.5594          | 0.7649   | 0.8003 |
+| 0.326         | 3.7778 | 340  | 0.5446          | 0.7661   | 0.8158 |
+| 0.2979        | 4.0    | 360  | 0.5750          | 0.7681   | 0.8145 |
+| 0.2457        | 4.2222 | 380  | 0.6121          | 0.7677   | 0.8140 |
+| 0.2383        | 4.4444 | 400  | 0.5861          | 0.7689   | 0.8118 |
+| 0.2396        | 4.6667 | 420  | 0.6161          | 0.7734   | 0.8156 |
+| 0.2311        | 4.8889 | 440  | 0.5909          | 0.7751   | 0.8121 |
+| 0.2139        | 5.1111 | 460  | 0.6411          | 0.7702   | 0.8164 |
+| 0.2038        | 5.3333 | 480  | 0.6462          | 0.7718   | 0.8154 |
+| 0.1884        | 5.5556 | 500  | 0.6443          | 0.7645   | 0.8043 |
+| 0.1889        | 5.7778 | 520  | 0.6588          | 0.7665   | 0.8064 |
+| 0.2081        | 6.0    | 540  | 0.6581          | 0.7665   | 0.8054 |
+
+
+### 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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+    "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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+  "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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+    "bias": true,
+    "dropout_prob": null,
+    "head_type": "classification",
+    "label2id": {
+      "Abusive": 1,
+      "Non-Abusive": 0
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+    "layers": 2,
+    "num_labels": 2,
+    "use_pooler": false
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+  "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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