Duplicate from autoevaluate/multi-class-classification
Browse filesCo-authored-by: Lewis Tunstall <[email protected]>
- .gitattributes +27 -0
- .gitignore +1 -0
- README.md +59 -0
- config.json +41 -0
- pytorch_model.bin +3 -0
- runs/May28_13-26-58_c59c2f6ceea3/1653744479.2715502/events.out.tfevents.1653744479.c59c2f6ceea3.92.1 +3 -0
- runs/May28_13-26-58_c59c2f6ceea3/events.out.tfevents.1653744479.c59c2f6ceea3.92.0 +3 -0
- runs/May28_13-30-29_c59c2f6ceea3/1653744636.3672242/events.out.tfevents.1653744636.c59c2f6ceea3.92.3 +3 -0
- runs/May28_13-30-29_c59c2f6ceea3/events.out.tfevents.1653744636.c59c2f6ceea3.92.2 +3 -0
- runs/May28_13-30-29_c59c2f6ceea3/events.out.tfevents.1653744792.c59c2f6ceea3.92.4 +3 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- training_args.bin +3 -0
- vocab.txt +0 -0
.gitattributes
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.gitignore
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checkpoint-*/
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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- emotion
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metrics:
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- accuracy
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duplicated_from: autoevaluate/multi-class-classification
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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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# multi-class-classification
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2009
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- Accuracy: 0.928
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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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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.2643 | 1.0 | 1000 | 0.2009 | 0.928 |
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### Framework versions
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- Transformers 4.19.2
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- Pytorch 1.11.0+cu113
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- Datasets 2.2.2
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- Tokenizers 0.12.1
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config.json
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{
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"_name_or_path": "distilbert-base-uncased",
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"hidden_dim": 3072,
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"id2label": {
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"0": "sadness",
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"1": "joy",
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"2": "love",
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"3": "anger",
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"4": "fear",
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"5": "surprise"
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},
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"initializer_range": 0.02,
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"label2id": {
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"sadness": 0,
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"joy": 1,
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"love": 2,
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"anger": 3,
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"fear": 4,
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"surprise": 5
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},
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.19.2",
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"vocab_size": 30522
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}
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tokenizer.json
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tokenizer_config.json
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{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "distilbert-base-uncased", "tokenizer_class": "DistilBertTokenizer"}
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training_args.bin
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vocab.txt
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