SeyedAli/Persian-Text-Emotion-Bert-V1

README.md

@@ -1,3 +1,73 @@
 ---
-license: mit
+base_model: HooshvareLab/bert-base-parsbert-uncased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- accuracy
+model-index:
+- name: Persian-Text-Sentiment-Bert-V1
+  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. -->
+
+# Persian-Text-Sentiment-Bert-V1
+
+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: 0.3265
+- Precision: 0.8727
+- Recall: 0.8716
+- F1-score: 0.8715
+- Accuracy: 0.8716
+
+## 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 | F1-score | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|
+| 0.3097        | 1.0   | 3491  | 0.3265          | 0.8727    | 0.8716 | 0.8715   | 0.8716   |
+| 0.2686        | 2.0   | 6982  | 0.3602          | 0.8785    | 0.8758 | 0.8756   | 0.8758   |
+| 0.2137        | 3.0   | 10473 | 0.3828          | 0.8759    | 0.8724 | 0.8721   | 0.8724   |
+| 0.1823        | 4.0   | 13964 | 0.5545          | 0.8637    | 0.8636 | 0.8636   | 0.8636   |
+| 0.1346        | 5.0   | 17455 | 0.6295          | 0.8572    | 0.8566 | 0.8566   | 0.8566   |
+| 0.1001        | 6.0   | 20946 | 0.8501          | 0.8606    | 0.8604 | 0.8604   | 0.8604   |
+| 0.071         | 7.0   | 24437 | 1.0192          | 0.8596    | 0.8594 | 0.8594   | 0.8594   |
+| 0.0604        | 8.0   | 27928 | 1.0449          | 0.8553    | 0.8553 | 0.8553   | 0.8553   |
+| 0.0312        | 9.0   | 31419 | 1.1677          | 0.8598    | 0.8598 | 0.8598   | 0.8598   |
+| 0.022         | 10.0  | 34910 | 1.2128          | 0.8593    | 0.8591 | 0.8591   | 0.8591   |
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3

config.json

@@ -0,0 +1,34 @@
+{
+  "_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": "negetive",
+    "1": "positive"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "negetive": 0,
+    "positive": 1
+  },
+  "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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+size 651439921

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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