End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- dstefa/New_York_Times_Topics
+metrics:
+- accuracy
+model-index:
+- name: DistilBERT base classify news topics - Devinit
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: New York Times Topics
+      type: dstefa/New_York_Times_Topics
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.913482481060606
+---
+
+<!-- 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. -->
+
+# DistilBERT base classify news topics - Devinit
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the New York Times Topics dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2871
+- Accuracy: 0.9135
+
+## 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: 1e-05
+- train_batch_size: 128
+- eval_batch_size: 128
+- 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 | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 0.386         | 1.0   | 1340  | 0.3275          | 0.8921   |
+| 0.2833        | 2.0   | 2680  | 0.2840          | 0.9033   |
+| 0.2411        | 3.0   | 4020  | 0.2694          | 0.9102   |
+| 0.2069        | 4.0   | 5360  | 0.2665          | 0.9114   |
+| 0.1796        | 5.0   | 6700  | 0.2657          | 0.9128   |
+| 0.1636        | 6.0   | 8040  | 0.2674          | 0.9142   |
+| 0.144         | 7.0   | 9380  | 0.2761          | 0.9129   |
+| 0.1277        | 8.0   | 10720 | 0.2820          | 0.9125   |
+| 0.1201        | 9.0   | 12060 | 0.2853          | 0.9136   |
+| 0.1104        | 10.0  | 13400 | 0.2871          | 0.9135   |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

config.json

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+{
+  "_name_or_path": "distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "Sports",
+    "1": "Arts, Culture, and Entertainment",
+    "2": "Business and Finance",
+    "3": "Health and Wellness",
+    "4": "Lifestyle and Fashion",
+    "5": "Science and Technology",
+    "6": "Politics",
+    "7": "Crime"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "Arts, Culture, and Entertainment": 1,
+    "Business and Finance": 2,
+    "Crime": 7,
+    "Health and Wellness": 3,
+    "Lifestyle and Fashion": 4,
+    "Politics": 6,
+    "Science and Technology": 5,
+    "Sports": 0
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "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": "4.36.2",
+  "vocab_size": 30522
+}

model.safetensors

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+size 267851024

runs/Jan19_19-23-05_5693c156d1dc/events.out.tfevents.1705692186.5693c156d1dc.464.0

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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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+      "normalized": false,
+      "rstrip": false,
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+      "special": true
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "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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