jamesbaskerville/classify-article-titles
Browse files- README.md +76 -0
- config.json +48 -0
- model.safetensors +3 -0
- special_tokens_map.json +15 -0
- spiece.model +3 -0
- tokenizer_config.json +58 -0
- trainer_state.json +247 -0
- training_args.bin +3 -0
README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: albert/albert-base-v2
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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model-index:
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- name: classify-articles
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# classify-articles
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This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3819
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- Accuracy: 0.9070
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- F1: 0.9061
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- Precision: 0.9126
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- Recall: 0.9070
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- Accuracy Label Economy: 0.9429
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- Accuracy Label Politics: 0.9574
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- Accuracy Label Science: 0.9362
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- Accuracy Label Sports: 0.96
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- Accuracy Label Technology: 0.6944
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Accuracy Label Economy | Accuracy Label Politics | Accuracy Label Science | Accuracy Label Sports | Accuracy Label Technology |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:----------------------:|:-----------------------:|:----------------------:|:---------------------:|:-------------------------:|
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| 1.3703 | 1.3072 | 100 | 1.3775 | 0.4930 | 0.4238 | 0.6100 | 0.4930 | 0.8 | 0.0213 | 0.7021 | 0.72 | 0.2222 |
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| 0.4329 | 2.6144 | 200 | 0.4495 | 0.8977 | 0.9004 | 0.9134 | 0.8977 | 0.9429 | 0.8936 | 0.9149 | 0.96 | 0.75 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.1
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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config.json
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{
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"_name_or_path": "albert/albert-base-v2",
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"architectures": [
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"AlbertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0,
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"bos_token_id": 2,
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"classifier_dropout_prob": 0.1,
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"down_scale_factor": 1,
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"embedding_size": 128,
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"eos_token_id": 3,
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"gap_size": 0,
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"hidden_act": "gelu_new",
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"hidden_dropout_prob": 0,
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"hidden_size": 768,
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"id2label": {
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"0": "economy",
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"1": "politics",
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"2": "science",
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"3": "sports",
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"4": "technology"
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},
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"initializer_range": 0.02,
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"inner_group_num": 1,
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"intermediate_size": 3072,
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"label2id": {
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"economy": 0,
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"politics": 1,
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"science": 2,
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"sports": 3,
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"technology": 4
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "albert",
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"net_structure_type": 0,
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"num_attention_heads": 12,
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"num_hidden_groups": 1,
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"num_hidden_layers": 12,
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"num_memory_blocks": 0,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.44.2",
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"type_vocab_size": 2,
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"vocab_size": 30000
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:7bf37bf834442ed64e7594dc101754a1a27db9fc60e46c578dae2c6a95326d59
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size 46753140
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special_tokens_map.json
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{
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"bos_token": "[CLS]",
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"cls_token": "[CLS]",
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"eos_token": "[SEP]",
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"mask_token": {
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"content": "[MASK]",
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"lstrip": true,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "<pad>",
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"sep_token": "[SEP]",
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"unk_token": "<unk>"
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}
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spiece.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:fefb02b667a6c5c2fe27602d28e5fb3428f66ab89c7d6f388e7c8d44a02d0336
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size 760289
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"3": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"4": {
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"content": "[MASK]",
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"lstrip": true,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"bos_token": "[CLS]",
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"eos_token": "[SEP]",
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"keep_accents": false,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "<pad>",
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"remove_space": true,
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"sep_token": "[SEP]",
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"sp_model_kwargs": {},
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"tokenizer_class": "AlbertTokenizer",
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"unk_token": "<unk>"
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}
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trainer_state.json
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