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README.md

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+---
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: ConfliBERT-Arabic-multilingual-uncased
+  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. -->
+
+# ConfliBERT-Arabic-multilingual-uncased
+
+This model is a fine-tuned version of [../models/bert-base-multilingual-uncased](https://huggingface.co/../models/bert-base-multilingual-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4586
+- Accuracy: 0.8897
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 944187
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.13.0.dev20220616
+- Datasets 2.8.0
+- Tokenizers 0.13.2

all_results.json

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+{
+    "epoch": 2.39,
+    "eval_accuracy": 0.8897356664039185,
+    "eval_loss": 0.45863792300224304,
+    "eval_runtime": 4431.9499,
+    "eval_samples": 662729,
+    "eval_samples_per_second": 149.534,
+    "eval_steps_per_second": 4.673,
+    "perplexity": 1.5819178230194426,
+    "train_loss": 0.6286116640401175,
+    "train_runtime": 410211.7129,
+    "train_samples": 12666977,
+    "train_samples_per_second": 73.655,
+    "train_steps_per_second": 2.302
+}

config.json

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+{
+  "_name_or_path": "../models/bert-base-multilingual-uncased",
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 105879
+}

eval_results.json

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+{
+    "epoch": 2.39,
+    "eval_accuracy": 0.8897356664039185,
+    "eval_loss": 0.45863792300224304,
+    "eval_runtime": 4431.9499,
+    "eval_samples": 662729,
+    "eval_samples_per_second": 149.534,
+    "eval_steps_per_second": 4.673,
+    "perplexity": 1.5819178230194426
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "pad_token_id": 0,
+  "transformers_version": "4.26.0"
+}

pytorch_model.bin

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+size 669926955

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "name_or_path": "../models/bert-base-multilingual-uncased",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

train_results.json

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+{
+    "epoch": 2.39,
+    "train_loss": 0.6286116640401175,
+    "train_runtime": 410211.7129,
+    "train_samples": 12666977,
+    "train_samples_per_second": 73.655,
+    "train_steps_per_second": 2.302
+}

training_args.bin

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+size 3567