SeyedAli/Persian-Text-Emotion-Bert-V1

README.md

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+---
+base_model: HooshvareLab/bert-base-parsbert-uncased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- accuracy
+model-index:
+- name: output
+  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. -->
+
+# output
+
+This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2551
+- Precision: 0.9362
+- Recall: 0.9360
+- Fscore: 0.9359
+- Accuracy: 0.9360
+
+## 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 348  | 0.3054          | 0.9166    | 0.9144 | 0.9136 | 0.9144   |
+| 0.5158        | 2.0   | 696  | 0.2551          | 0.9362    | 0.9360 | 0.9359 | 0.9360   |
+| 0.1469        | 3.0   | 1044 | 0.3670          | 0.9283    | 0.9259 | 0.9245 | 0.9259   |
+| 0.1469        | 4.0   | 1392 | 0.3833          | 0.9331    | 0.9317 | 0.9307 | 0.9317   |
+| 0.0453        | 5.0   | 1740 | 0.4241          | 0.9356    | 0.9345 | 0.9342 | 0.9345   |
+| 0.0237        | 6.0   | 2088 | 0.3750          | 0.9441    | 0.9439 | 0.9437 | 0.9439   |
+| 0.0237        | 7.0   | 2436 | 0.3986          | 0.9389    | 0.9388 | 0.9385 | 0.9388   |
+| 0.009         | 8.0   | 2784 | 0.4100          | 0.9407    | 0.9403 | 0.9397 | 0.9403   |
+| 0.0053        | 9.0   | 3132 | 0.4005          | 0.9403    | 0.9403 | 0.9401 | 0.9403   |
+| 0.0053        | 10.0  | 3480 | 0.3986          | 0.9410    | 0.9410 | 0.9408 | 0.9410   |
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "HooshvareLab/bert-base-parsbert-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "joy",
+    "1": "sad",
+    "2": "anger",
+    "3": "disgust",
+    "4": "fear",
+    "5": "surprise"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "anger": 2,
+    "disgust": 3,
+    "fear": 4,
+    "joy": 0,
+    "sad": 1,
+    "surprise": 5
+  },
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 100000
+}

pytorch_model.bin

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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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+{
+  "add_special_tokens": true,
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 100,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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