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firqaaa
/
indo-setfit-bert-base-p1

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
firqaaa commited on
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Add SetFit model

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Files changed (13) hide show
  1. 1_Pooling/config.json +9 -0
  2. README.md +353 -0
  3. config.json +47 -0
  4. config_sentence_transformers.json +7 -0
  5. config_setfit.json +7 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +64 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false
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+ }
README.md ADDED
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+ ---
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+ library_name: setfit
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: Ini adalah kisah tentang dua orang yang tidak selaras dan tidak memiliki kesempatan
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+ sendirian, tetapi bersama-sama mereka luar biasa.
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+ - text: ia tidak percaya pada dirinya sendiri, ia tidak memiliki rasa humor ... ia
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+ hanya merasa bosan.
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+ - text: Keberanian band dalam menghadapi represi resmi sangat menginspirasi, terutama
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+ bagi para hippie yang telah menua (termasuk saya sendiri).
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+ - text: film yang cepat, lucu, dan sangat menghibur.
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+ - text: film ini mencapai dampak yang sama besar dengan menyimpan pemikiran-pemikiran
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+ ini tersembunyi seperti halnya film "Quills" yang menunjukkannya.
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: firqaaa/indo-sentence-bert-base
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+ model-index:
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+ - name: SetFit with firqaaa/indo-sentence-bert-base
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.8
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with firqaaa/indo-sentence-bert-base
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 2 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:--------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | positif | <ul><li>'benar-benar lucu'</li><li>'gulungan dari sebuah tong tong yang tersesat'</li><li>', mereka menemukan rute-rute baru melalui lingkungan yang sudah dikenal'</li></ul> |
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+ | negatif | <ul><li>'tidak menarik atau berbau tidak sedap'</li><li>"telah melakukan kesalahan nyaris fatal dengan menjadi apa yang orang Inggris sebut 'terlalu pintar setengah mati'."</li><li>'untuk roboh'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.8 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("firqaaa/indo-setfit-bert-base-p1")
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+ # Run inference
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+ preds = model("film yang cepat, lucu, dan sangat menghibur.")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:-------|:----|
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+ | Word count | 1 | 9.4825 | 51 |
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+
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+ | Label | Training Sample Count |
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+ |:--------|:----------------------|
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+ | negatif | 200 |
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+ | positif | 200 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (32, 32)
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+ - num_epochs: (3, 3)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: True
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:-------:|:--------:|:-------------:|:---------------:|
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+ | 0.0004 | 1 | 0.3079 | - |
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+ | 0.0199 | 50 | 0.3644 | - |
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+ | 0.0398 | 100 | 0.2816 | - |
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+ | 0.0597 | 150 | 0.2254 | - |
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+ | 0.0796 | 200 | 0.1798 | - |
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+ | 0.0995 | 250 | 0.0478 | - |
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+ | 0.1194 | 300 | 0.0049 | - |
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+ | 0.1393 | 350 | 0.0016 | - |
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+ | 0.1592 | 400 | 0.0011 | - |
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+ | 0.1791 | 450 | 0.0005 | - |
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+ | 0.1990 | 500 | 0.0003 | - |
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+ | 0.2189 | 550 | 0.0004 | - |
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+ | 0.2388 | 600 | 0.0003 | - |
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+ | 0.2587 | 650 | 0.0003 | - |
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+ | 0.2786 | 700 | 0.0001 | - |
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+ | 0.2984 | 750 | 0.0002 | - |
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+ | 0.3183 | 800 | 0.0001 | - |
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+ | **1.0** | **2513** | **-** | **0.2622** |
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+ | 3.0 | 7539 | - | 0.2744 |
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+
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+ * The bold row denotes the saved checkpoint.
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+ ### Framework Versions
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+ - Python: 3.10.13
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+ - SetFit: 1.0.3
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+ - Sentence Transformers: 2.3.1
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+ - Transformers: 4.36.2
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+ - PyTorch: 2.1.2+cu121
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+ - Datasets: 2.16.1
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+ - Tokenizers: 0.15.0
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+
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+ ## Citation
322
+
323
+ ### BibTeX
324
+ ```bibtex
325
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
326
+ doi = {10.48550/ARXIV.2209.11055},
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+ url = {https://arxiv.org/abs/2209.11055},
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+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
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+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
334
+ }
335
+ ```
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+
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+ <!--
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+ ## Glossary
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+
340
+ *Clearly define terms in order to be accessible across audiences.*
341
+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
346
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
347
+ -->
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+
349
+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
config.json ADDED
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+ {
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+ "_name_or_path": "checkpoints/step_2513",
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+ "architectures": [
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+ "BertModel"
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+ ],
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+ "id2label": {
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+ "0": "LABEL_0",
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+ "1": "LABEL_1",
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+ "2": "LABEL_2",
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+ "3": "LABEL_3",
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+ "4": "LABEL_4"
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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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+ "pooler_type": "first_token_transform",
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+ "position_embedding_type": "absolute",
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+ "transformers_version": "4.36.2",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 50000
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+ }
config_sentence_transformers.json ADDED
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+ {
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+ "__version__": {
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+ "sentence_transformers": "2.2.2",
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+ "transformers": "4.20.1",
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+ "pytorch": "1.11.0"
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+ }
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+ }
config_setfit.json ADDED
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+ {
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+ "normalize_embeddings": false,
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+ "labels": [
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+ "negatif",
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+ "positif"
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+ ]
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+ }
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