Model save

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-cased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: my-finetuned-FinanceNews-distilbert
+  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. -->
+
+# my-finetuned-FinanceNews-distilbert
+
+This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3800
+- Accuracy: 0.8557
+- F1: 0.8547
+- Precision: 0.8548
+- Recall: 0.8557
+- Classification Report:               precision    recall  f1-score   support
+
+     Class 0       0.87      0.90      0.88        87
+     Class 1       0.87      0.90      0.88       268
+     Class 2       0.83      0.76      0.79       151
+
+    accuracy                           0.86       506
+   macro avg       0.85      0.85      0.85       506
+weighted avg       0.85      0.86      0.85       506
+
+
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Classification Report                                                                                                                                                                                                                                                                                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| 0.7555        | 1.0   | 72   | 0.4573          | 0.8241   | 0.8235 | 0.8232    | 0.8241 |               precision    recall  f1-score   support
+
+     Class 0       0.83      0.84      0.83        87
+     Class 1       0.85      0.87      0.86       268
+     Class 2       0.78      0.74      0.76       151
+
+    accuracy                           0.82       506
+   macro avg       0.82      0.82      0.82       506
+weighted avg       0.82      0.82      0.82       506
+ |
+| 0.4201        | 2.0   | 144  | 0.3800          | 0.8557   | 0.8547 | 0.8548    | 0.8557 |               precision    recall  f1-score   support
+
+     Class 0       0.87      0.90      0.88        87
+     Class 1       0.87      0.90      0.88       268
+     Class 2       0.83      0.76      0.79       151
+
+    accuracy                           0.86       506
+   macro avg       0.85      0.85      0.85       506
+weighted avg       0.85      0.86      0.85       506
+ |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.2.2
+- Datasets 2.20.0
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "distilbert-base-cased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "vocab_size": 28996
+}

model.safetensors

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runs/Jul06_00-14-13_Ghazis-MBP.lan/events.out.tfevents.1720214117.Ghazis-MBP.lan.5822.0

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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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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "rstrip": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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