Instructions to use bob-bob-bob-3/buburayam2024_indobtwt_7_reprsample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bob-bob-bob-3/buburayam2024_indobtwt_7_reprsample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bob-bob-bob-3/buburayam2024_indobtwt_7_reprsample")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bob-bob-bob-3/buburayam2024_indobtwt_7_reprsample") model = AutoModelForSequenceClassification.from_pretrained("bob-bob-bob-3/buburayam2024_indobtwt_7_reprsample") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +66 -0
- config.json +40 -0
- model.safetensors +3 -0
- runs/Jun19_19-26-00_80516ee61c48/events.out.tfevents.1718825164.80516ee61c48.721.10 +3 -0
- training_args.bin +3 -0
README.md
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---
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license: apache-2.0
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base_model: indolem/indobertweet-base-uncased
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tags:
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- generated_from_trainer
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model-index:
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- name: buburayam2024_indobtwt_7_reprsample
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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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# buburayam2024_indobtwt_7_reprsample
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This model is a fine-tuned version of [indolem/indobertweet-base-uncased](https://huggingface.co/indolem/indobertweet-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.9512
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- F1 macro: 0.4163
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- Weighted: 0.6765
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- Balanced accuracy: 0.4952
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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: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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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: 7
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | F1 macro | Weighted | Balanced accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|
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| 1.1941 | 1.0 | 186 | 0.9804 | 0.3927 | 0.6619 | 0.4570 |
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| 0.8084 | 2.0 | 372 | 0.9803 | 0.4381 | 0.7056 | 0.4876 |
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| 0.3287 | 3.0 | 558 | 1.3391 | 0.3943 | 0.6546 | 0.4784 |
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| 0.1292 | 4.0 | 744 | 1.4696 | 0.4297 | 0.6830 | 0.5058 |
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| 0.0639 | 5.0 | 930 | 1.8854 | 0.4131 | 0.6647 | 0.4981 |
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| 0.0088 | 6.0 | 1116 | 1.9583 | 0.4109 | 0.6704 | 0.4882 |
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| 0.0156 | 7.0 | 1302 | 1.9512 | 0.4163 | 0.6765 | 0.4952 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.0+cu121
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- Datasets 2.20.0
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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": "indolem/indobertweet-base-uncased",
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"eos_token_ids": 0,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "Sumber Daya Alam",
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"1": "Politik",
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"2": "Demografi",
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"3": "Pertahanan dan Keamanan",
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"4": "Ideologi",
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"5": "Ekonomi",
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"6": "Sosial Budaya",
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"7": "Geografi"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"output_past": true,
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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": "4.41.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 31923
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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:fa1393149f68aeeaed8044697ebff3291ca236acdf83b16a082f3cd8187c9ba7
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size 442280976
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runs/Jun19_19-26-00_80516ee61c48/events.out.tfevents.1718825164.80516ee61c48.721.10
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version https://git-lfs.github.com/spec/v1
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oid sha256:346c25c65a84680d1151fa2297ff875ab2338f3064722a598b8200b29dca2d3e
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size 35998
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:e675622df8aa12b652e77bd3784c42a2400269bde13b6976ce67e8f7abf1fe27
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size 5176
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