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firqaaa
/
indo-setfit-bert-base-p3

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
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firqaaa commited on
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Add SetFit model

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Files changed (13) hide show
  1. 1_Pooling/config.json +7 -0
  2. README.md +628 -0
  3. config.json +47 -0
  4. config_sentence_transformers.json +7 -0
  5. config_setfit.json +11 -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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+ }
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: Aku sudah lebih tua dan hidupku sangat berbeda. Aku bisa merasakan betapa
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+ takjubnya aku pagi itu
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+ - text: Saya merasa cukup href http kata-kata yang tak terucapkan disimpan di dalam
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+ - text: Aku melihat ke dalam dompetku dan aku merasakan hawa dingin
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+ - text: Aku menurunkan Erik dengan perasaan agak tidak puas dengan malam itu
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+ - text: Aku bertanya-tanya apa yang siswa lain di kelasku rasakan ketika aku tidak
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+ takut untuk memberikan jawaban di luar sana
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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: 0.718
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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:** 6 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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+ | kesedihan | <ul><li>'Saya merasa agak kecewa, saya rasa harus menyerahkan sesuatu yang tidak menarik hanya untuk memenuhi tenggat waktu'</li><li>'Aku merasa seperti aku telah cukup lalai terhadap blogku dan aku hanya mengatakan bahwa kita di sini hidup dan bahagia'</li><li>'Aku tahu dan aku selalu terkoyak karenanya karena aku merasa tidak berdaya dan tidak berguna'</li></ul> |
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+ | sukacita | <ul><li>'aku mungkin tidak merasa begitu keren'</li><li>'saya merasa baik-baik saja'</li><li>'saya merasa seperti saya seorang ibu dengan mengorbankan produktivitas'</li></ul> |
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+ | cinta | <ul><li>'aku merasa mencintaimu'</li><li>'aku akan merasa sangat nostalgia di usia yang begitu muda'</li><li>'Saya merasa diberkati bahwa saya tinggal di Amerika memiliki keluarga yang luar biasa dan Dorothy Kelsey adalah bagian dari hidup saya'</li></ul> |
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+ | amarah | <ul><li>'Aku terlalu memikirkan cara dudukku, suaraku terdengar jika ada makanan di mulutku, dan perasaan bahwa aku harus berjalan ke semua orang agar tidak bersikap kasar'</li><li>'aku merasa memberontak sedikit kesal gila terkurung'</li><li>'Aku merasakan perasaan itu muncul kembali dari perasaan paranoid dan cemburu yang penuh kebencian yang selalu menyiksaku tanpa henti'</li></ul> |
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+ | takut | <ul><li>'aku merasa seperti diserang oleh landak titanium'</li><li>'Aku membiarkan diriku memikirkan perilakuku terhadapmu saat kita masih kecil. Aku merasakan campuran aneh antara rasa bersalah dan kekaguman atas ketangguhanmu'</li><li>'saya marah karena majikan saya tidak berinvestasi pada kami sama sekali, gaji pelatihan, kenaikan hari libur bank dan rasanya seperti ketidakadilan sehingga saya merasa tidak berdaya'</li></ul> |
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+ | kejutan | <ul><li>'Aku membaca bagian ol feefyefo Aku merasa takjub melihat betapa aku bisa mengoceh dan betapa transparannya aku dalam hidupku'</li><li>'saya menemukan seni di sisi lain saya merasa sangat terkesan dengan karya saya'</li><li>'aku merasa penasaran, bersemangat dan tidak sabar'</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** | 0.718 |
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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-p3")
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+ # Run inference
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+ preds = model("Aku melihat ke dalam dompetku dan aku merasakan hawa dingin")
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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 | 2 | 16.7928 | 56 |
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+
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+ | Label | Training Sample Count |
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+ |:----------|:----------------------|
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+ | kesedihan | 300 |
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+ | sukacita | 300 |
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+ | cinta | 300 |
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+ | amarah | 300 |
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+ | takut | 300 |
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+ | kejutan | 300 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (128, 128)
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+ - num_epochs: (1, 1)
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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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+ | 0.0000 | 1 | 0.2927 | - |
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+ | 0.0024 | 50 | 0.2605 | - |
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+ | 0.0047 | 100 | 0.2591 | - |
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+ | 0.0071 | 150 | 0.2638 | - |
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+ | 0.0095 | 200 | 0.245 | - |
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+ | 0.0119 | 250 | 0.226 | - |
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+ | 0.0142 | 300 | 0.222 | - |
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+ | 0.0166 | 350 | 0.1968 | - |
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+ | 0.0190 | 400 | 0.1703 | - |
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+ | 0.0213 | 450 | 0.1703 | - |
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+ | 0.0237 | 500 | 0.1587 | - |
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+ | 0.0261 | 550 | 0.1087 | - |
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+ | 0.0284 | 600 | 0.1203 | - |
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+ | 0.0308 | 650 | 0.0844 | - |
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+ | 0.0332 | 700 | 0.0696 | - |
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+ | 0.0356 | 750 | 0.0606 | - |
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+ | 0.0379 | 800 | 0.0333 | - |
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+ | 0.0403 | 850 | 0.0453 | - |
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+ | 0.0427 | 900 | 0.033 | - |
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+ | 0.0450 | 950 | 0.0142 | - |
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+ | 0.0474 | 1000 | 0.004 | - |
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+ | 0.0498 | 1050 | 0.0097 | - |
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+ | 0.0521 | 1100 | 0.0065 | - |
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+ | 0.0545 | 1150 | 0.0081 | - |
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+ | 0.0569 | 1200 | 0.0041 | - |
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+ | 0.0593 | 1250 | 0.0044 | - |
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+ | 0.0616 | 1300 | 0.0013 | - |
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+ | 0.0640 | 1350 | 0.0024 | - |
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+ | 0.0664 | 1400 | 0.001 | - |
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+ | 0.0687 | 1450 | 0.0012 | - |
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+ | 0.0711 | 1500 | 0.0013 | - |
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+ | 0.0735 | 1550 | 0.0006 | - |
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+ | 0.0759 | 1600 | 0.0033 | - |
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+ | 0.0782 | 1650 | 0.0006 | - |
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+ | 0.0806 | 1700 | 0.0013 | - |
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+ | 0.0830 | 1750 | 0.0008 | - |
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+ | 0.0853 | 1800 | 0.0006 | - |
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+ | 0.0877 | 1850 | 0.0008 | - |
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+ | 0.0901 | 1900 | 0.0004 | - |
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+ | 0.0924 | 1950 | 0.0005 | - |
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+ | 0.0948 | 2000 | 0.0004 | - |
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+ | 0.0972 | 2050 | 0.0002 | - |
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+ | 0.0996 | 2100 | 0.0002 | - |
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+ | 0.1019 | 2150 | 0.0003 | - |
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+ | 0.1043 | 2200 | 0.0006 | - |
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+ | 0.1067 | 2250 | 0.0005 | - |
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+ | 0.1090 | 2300 | 0.0003 | - |
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+ | 0.1114 | 2350 | 0.0018 | - |
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+ | 0.1138 | 2400 | 0.0003 | - |
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+ | 0.1161 | 2450 | 0.0002 | - |
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+ | 0.1185 | 2500 | 0.0018 | - |
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+ | 0.1209 | 2550 | 0.0003 | - |
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+ | 0.1233 | 2600 | 0.0008 | - |
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+ | 0.1256 | 2650 | 0.0002 | - |
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+ | 0.1280 | 2700 | 0.0007 | - |
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+ | 0.1304 | 2750 | 0.006 | - |
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+ | 0.1967 | 4150 | 0.0006 | - |
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+ | 0.1991 | 4200 | 0.0001 | - |
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+ | 0.2015 | 4250 | 0.0 | - |
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+ | 0.2181 | 4600 | 0.0003 | - |
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+ | 0.2204 | 4650 | 0.0018 | - |
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+ | 0.2228 | 4700 | 0.0003 | - |
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+ | 0.2276 | 4800 | 0.0001 | - |
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+ | 0.2299 | 4850 | 0.0006 | - |
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+ | 0.2323 | 4900 | 0.0001 | - |
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+ | 0.2347 | 4950 | 0.0007 | - |
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+ | 0.2370 | 5000 | 0.0001 | - |
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+ | 0.2394 | 5050 | 0.0 | - |
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+ | 0.2465 | 5200 | 0.0003 | - |
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+ | 0.2584 | 5450 | 0.0004 | - |
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+ | 0.2655 | 5600 | 0.0 | - |
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+ | 0.2702 | 5700 | 0.0 | - |
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+ | 0.2726 | 5750 | 0.0002 | - |
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+ | 0.2750 | 5800 | 0.0 | - |
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+ | 0.2987 | 6300 | 0.0001 | - |
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+ | 0.3010 | 6350 | 0.0003 | - |
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+ | 0.3034 | 6400 | 0.0048 | - |
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+ | 0.3390 | 7150 | 0.0011 | - |
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+ | 0.3603 | 7600 | 0.0 | - |
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+ | 0.3793 | 8000 | 0.0 | - |
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+ | 0.4124 | 8700 | 0.0001 | - |
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+ | **1.0** | **21094** | **-** | **0.2251** |
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+
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+ * The bold row denotes the saved checkpoint.
587
+ ### Framework Versions
588
+ - Python: 3.10.13
589
+ - SetFit: 1.0.3
590
+ - Sentence Transformers: 2.2.2
591
+ - 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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+
596
+ ## Citation
597
+
598
+ ### BibTeX
599
+ ```bibtex
600
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
601
+ doi = {10.48550/ARXIV.2209.11055},
602
+ url = {https://arxiv.org/abs/2209.11055},
603
+ 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},
607
+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
614
+
615
+ *Clearly define terms in order to be accessible across audiences.*
616
+ -->
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+
618
+ <!--
619
+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
622
+ -->
623
+
624
+ <!--
625
+ ## Model Card Contact
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+
627
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
628
+ -->
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+ "pad_token_type_id": 0,
55
+ "padding_side": "right",
56
+ "sep_token": "[SEP]",
57
+ "stride": 0,
58
+ "strip_accents": null,
59
+ "tokenize_chinese_chars": true,
60
+ "tokenizer_class": "BertTokenizer",
61
+ "truncation_side": "right",
62
+ "truncation_strategy": "longest_first",
63
+ "unk_token": "[UNK]"
64
+ }
vocab.txt ADDED
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