AraBERT-MADAR

README.md

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+---
+base_model: aubmindlab/bert-base-arabert
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: results
+  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. -->
+
+# results
+
+This model is a fine-tuned version of [aubmindlab/bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.4485
+- Accuracy: 0.7656
+- Precision: 0.7688
+- Recall: 0.7656
+- F1: 0.7650
+- Mrr: 0.8440
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 320
+- num_epochs: 12
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     | Mrr    |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
+| 0.9496        | 1.0   | 2250  | 0.9448          | 0.69     | 0.7197    | 0.69   | 0.6896 | 0.8003 |
+| 0.7839        | 2.0   | 4500  | 0.8385          | 0.7      | 0.7302    | 0.7    | 0.7032 | 0.8101 |
+| 0.4602        | 3.0   | 6750  | 0.9599          | 0.745    | 0.7524    | 0.745  | 0.7421 | 0.8346 |
+| 0.4453        | 4.0   | 9000  | 0.9992          | 0.7325   | 0.7474    | 0.7325 | 0.7353 | 0.8342 |
+| 0.3919        | 5.0   | 11250 | 1.2636          | 0.7425   | 0.7551    | 0.7425 | 0.7413 | 0.8312 |
+| 0.313         | 6.0   | 13500 | 1.3639          | 0.7625   | 0.7679    | 0.7625 | 0.7628 | 0.8442 |
+| 0.2186        | 7.0   | 15750 | 1.6281          | 0.745    | 0.7566    | 0.745  | 0.7461 | 0.8369 |
+| 0.1942        | 8.0   | 18000 | 1.5611          | 0.775    | 0.7822    | 0.775  | 0.7752 | 0.8486 |
+| 0.128         | 9.0   | 20250 | 1.7601          | 0.74     | 0.7504    | 0.74   | 0.7412 | 0.8341 |
+| 0.0598        | 10.0  | 22500 | 1.6894          | 0.7725   | 0.7761    | 0.7725 | 0.7725 | 0.8548 |
+| 0.0699        | 11.0  | 24750 | 1.8025          | 0.765    | 0.7698    | 0.765  | 0.7645 | 0.8460 |
+| 0.0292        | 12.0  | 27000 | 1.8754          | 0.76     | 0.7621    | 0.76   | 0.7592 | 0.8451 |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

config.json

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+{
+  "_name_or_path": "aubmindlab/bert-base-arabert",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "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",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8,
+    "LABEL_9": 9
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 64000
+}

model.safetensors

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training_args.bin

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