End of training

README.md

@@ -1,3 +1,71 @@
 ---
 license: mit
+base_model: roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: roberta-base_topic_classification_nyt_news
+  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. -->
+
+# roberta-base_topic_classification_nyt_news
+
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3797
+- Accuracy: 0.9094
+- F1: 0.9092
+- Precision: 0.9098
+- Recall: 0.9094
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step   | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.3192        | 1.0   | 20480  | 0.4078          | 0.8865   | 0.8859 | 0.8892    | 0.8865 |
+| 0.2863        | 2.0   | 40960  | 0.4271          | 0.8972   | 0.8970 | 0.8982    | 0.8972 |
+| 0.1979        | 3.0   | 61440  | 0.3797          | 0.9094   | 0.9092 | 0.9098    | 0.9094 |
+| 0.1239        | 4.0   | 81920  | 0.3981          | 0.9117   | 0.9113 | 0.9114    | 0.9117 |
+| 0.1472        | 5.0   | 102400 | 0.4033          | 0.9137   | 0.9135 | 0.9134    | 0.9137 |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.1.0+cu121
+- Datasets 2.12.0
+- Tokenizers 0.13.2

config.json

@@ -0,0 +1,48 @@
+{
+  "_name_or_path": "roberta-base",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "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,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.32.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

pytorch_model.bin

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+size 498676526

special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

training_args.bin

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