Add SetFit model
Browse files- 1_Pooling/config.json +7 -0
- README.md +331 -0
- config.json +47 -0
- config_sentence_transformers.json +7 -0
- config_setfit.json +10 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +64 -0
- vocab.txt +0 -0
1_Pooling/config.json
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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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}
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README.md
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| 1 |
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---
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| 2 |
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library_name: setfit
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| 3 |
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tags:
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| 4 |
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- setfit
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| 5 |
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- sentence-transformers
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| 6 |
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- text-classification
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| 7 |
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- generated_from_setfit_trainer
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| 8 |
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metrics:
|
| 9 |
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- accuracy
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| 10 |
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widget:
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| 11 |
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- text: aku hanya menyukai setiap menit film ini.
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| 12 |
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- text: bioskop orang dalam kondisi terbaiknya.
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| 13 |
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- text: bukan untuk orang yang mudah tersinggung atau mudah tersinggung, ini adalah
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| 14 |
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pemeriksaan yang berani dan berkepanjangan terhadap budaya yang diidolakan, kebencian
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| 15 |
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terhadap diri sendiri, dan politik seksual.
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| 16 |
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- text: itu curang.
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| 17 |
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- text: Meskipun penduduk setempat akan senang melihat situs-situs Cleveland, seluruh
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| 18 |
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dunia akan menikmati komedi bertempo cepat dengan keunikan yang mungkin membuat
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| 19 |
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iri para coen bersaudara yang telah memenangkan penghargaan.
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| 20 |
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pipeline_tag: text-classification
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| 21 |
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inference: true
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| 22 |
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base_model: firqaaa/indo-sentence-bert-base
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| 23 |
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model-index:
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| 24 |
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- name: SetFit with firqaaa/indo-sentence-bert-base
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| 25 |
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results:
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| 26 |
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- task:
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| 27 |
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type: text-classification
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| 28 |
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name: Text Classification
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| 29 |
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dataset:
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| 30 |
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name: Unknown
|
| 31 |
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type: unknown
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| 32 |
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split: test
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| 33 |
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metrics:
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| 34 |
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- type: accuracy
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| 35 |
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value: 0.3425339366515837
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| 36 |
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name: Accuracy
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| 37 |
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---
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| 38 |
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| 39 |
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# SetFit with firqaaa/indo-sentence-bert-base
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| 40 |
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| 41 |
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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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.
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| 42 |
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The model has been trained using an efficient few-shot learning technique that involves:
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| 44 |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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| 46 |
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2. Training a classification head with features from the fine-tuned Sentence Transformer.
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| 47 |
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## Model Details
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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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| 53 |
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- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
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- **Maximum Sequence Length:** 512 tokens
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| 55 |
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- **Number of Classes:** 5 classes
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| 56 |
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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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| 59 |
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### Model Sources
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| 61 |
+
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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| 63 |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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| 64 |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
|
| 65 |
+
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| 66 |
+
### Model Labels
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| 67 |
+
| Label | Examples |
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| 68 |
+
|:---------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| sangat positif | <ul><li>"tema universal untuk menjadi orang yang lebih baik melalui cinta belum pernah difilmkan dengan cara yang lebih menarik daripada di `baran. '"</li><li>'hangat dalam penggambaran manusia sehari-hari yang penuh kasih namun tidak konsisten, santai dalam langkah tenang yang sempurna, dan bangga dalam pesannya.'</li><li>'kecerdasan yang mendalam serta kasih sayang yang hangat dan menyelimuti terhembus dari setiap bingkainya.'</li></ul> |
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| sangat negatif | <ul><li>'sebuah film kejam yang dibuat oleh seseorang yang pasti membaca catcher in the rye tetapi jelas-jelas menderita disleksia'</li><li>"all the queen's men adalah film perang kemunduran yang gagal dalam berbagai tingkatan, sehingga harus membayar ganti rugi kepada pemirsa."</li><li>'... ini bahkan bukan film yang bisa kita nikmati sebagai pelarian ringan; ini adalah ketakutan dan frustrasi yang dipicu hingga tingkat yang tidak dapat ditoleransi.'</li></ul> |
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| positif | <ul><li>'direktur kredit ramsay karena mengambil cerita yang terkadang mustahil dan membuatnya terasa realistis.'</li><li>'patut diberi hormat hanya karena mencoba menjadi lebih kompleks daripada film rata-rata Anda.'</li><li>'tapi secara keseluruhan, Anda akan menyukai film ini.'</li></ul> |
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| negatif | <ul><li>'semua orang harus disalahkan di sini.'</li><li>'tontonan hingar bingar -lrb- di acara TV -rrb- biasanya telah diragi oleh pesona yang secara mencolok hilang dari ledakan layar lebar gadis-gadis itu.'</li><li>'alur cerita, karakter, drama, emosi, ide – semuanya tidak relevan dengan pengalaman melihat raja kalajengking.'</li></ul> |
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| netral | <ul><li>'affleck hanya menciptakan garis besar untuk sebuah peran yang masih perlu ia kembangkan, sebuah peran yang dengan mudah dipenuhi dengan otoritas.'</li><li>'sutradara oliver parker bekerja keras untuk mengubah kehidupan menjadi pentingnya bersikap sungguh-sungguh sehingga dia mungkin menarik satu atau dua otot.'</li><li>'clayburgh dan tambor adalah pemain yang menawan; tak satu pun dari mereka pantas menerima Eric Schaeffer.'</li></ul> |
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| 74 |
+
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| 75 |
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## Evaluation
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| 76 |
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### Metrics
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| 78 |
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| Label | Accuracy |
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| 79 |
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|:--------|:---------|
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| **all** | 0.3425 |
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| 81 |
+
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| 82 |
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## Uses
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| 83 |
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| 84 |
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### Direct Use for Inference
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| 85 |
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| 86 |
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First install the SetFit library:
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| 87 |
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| 88 |
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```bash
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| 89 |
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pip install setfit
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| 90 |
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```
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| 91 |
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| 92 |
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Then you can load this model and run inference.
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| 93 |
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| 94 |
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```python
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| 95 |
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from setfit import SetFitModel
|
| 96 |
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| 97 |
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# Download from the 🤗 Hub
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| 98 |
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model = SetFitModel.from_pretrained("firqaaa/indo-setfit-bert-base-p2")
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| 99 |
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# Run inference
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| 100 |
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preds = model("itu curang.")
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| 101 |
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```
|
| 102 |
+
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| 103 |
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<!--
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| 104 |
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### Downstream Use
|
| 105 |
+
|
| 106 |
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*List how someone could finetune this model on their own dataset.*
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| 107 |
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-->
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| 108 |
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| 109 |
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<!--
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| 110 |
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### Out-of-Scope Use
|
| 111 |
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| 112 |
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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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| 113 |
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-->
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<!--
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| 116 |
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## Bias, Risks and Limitations
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| 117 |
+
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| 118 |
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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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| 119 |
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-->
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| 120 |
+
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| 121 |
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<!--
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| 122 |
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### Recommendations
|
| 123 |
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| 124 |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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| 125 |
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-->
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| 126 |
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| 127 |
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## Training Details
|
| 128 |
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|
| 129 |
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### Training Set Metrics
|
| 130 |
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| Training set | Min | Median | Max |
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| 131 |
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|:-------------|:----|:-------|:----|
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| 132 |
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| Word count | 2 | 15.476 | 46 |
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| 133 |
+
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| 134 |
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| Label | Training Sample Count |
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| 135 |
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|:---------------|:----------------------|
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| 136 |
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| sangat negatif | 200 |
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| negatif | 200 |
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| netral | 200 |
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| positif | 200 |
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| sangat positif | 200 |
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| 141 |
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| 142 |
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### Training Hyperparameters
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| 143 |
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- batch_size: (128, 32)
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| 144 |
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- num_epochs: (1, 8)
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| 145 |
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- max_steps: -1
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| 146 |
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- sampling_strategy: oversampling
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| 147 |
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- body_learning_rate: (2e-05, 5e-06)
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| 148 |
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- head_learning_rate: 2e-05
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| 149 |
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- loss: CosineSimilarityLoss
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| 150 |
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- distance_metric: cosine_distance
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- margin: 0.25
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| 152 |
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- end_to_end: False
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| 153 |
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- use_amp: False
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| 154 |
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- warmup_proportion: 0.1
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| 155 |
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- seed: 42
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| 156 |
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- eval_max_steps: -1
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| 157 |
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- load_best_model_at_end: True
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| 158 |
+
|
| 159 |
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### Training Results
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| 160 |
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| Epoch | Step | Training Loss | Validation Loss |
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| 161 |
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|:-------:|:--------:|:-------------:|:---------------:|
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| 0.0002 | 1 | 0.3317 | - |
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| 0.008 | 50 | 0.2883 | - |
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| 0.016 | 100 | 0.2625 | - |
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| 0.024 | 150 | 0.2516 | - |
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| 0.032 | 200 | 0.2075 | - |
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| 0.04 | 250 | 0.184 | - |
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| 0.048 | 300 | 0.1632 | - |
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| 0.056 | 350 | 0.1105 | - |
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| 0.064 | 400 | 0.1109 | - |
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| 0.072 | 450 | 0.0934 | - |
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| 0.08 | 500 | 0.0518 | - |
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| 0.088 | 550 | 0.0246 | - |
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| 0.096 | 600 | 0.0133 | - |
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| 0.104 | 650 | 0.0056 | - |
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| 0.112 | 700 | 0.006 | - |
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| 0.12 | 750 | 0.0072 | - |
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| 0.128 | 800 | 0.0179 | - |
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| 0.136 | 850 | 0.0025 | - |
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| 0.144 | 900 | 0.0019 | - |
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| 0.152 | 950 | 0.0008 | - |
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| 0.16 | 1000 | 0.0009 | - |
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| 0.168 | 1050 | 0.0016 | - |
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| 0.176 | 1100 | 0.0008 | - |
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| 0.184 | 1150 | 0.0009 | - |
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| 0.192 | 1200 | 0.0006 | - |
|
| 187 |
+
| 0.2 | 1250 | 0.0112 | - |
|
| 188 |
+
| 0.208 | 1300 | 0.0007 | - |
|
| 189 |
+
| 0.216 | 1350 | 0.0005 | - |
|
| 190 |
+
| 0.224 | 1400 | 0.0006 | - |
|
| 191 |
+
| 0.232 | 1450 | 0.0004 | - |
|
| 192 |
+
| 0.24 | 1500 | 0.0003 | - |
|
| 193 |
+
| 0.248 | 1550 | 0.0111 | - |
|
| 194 |
+
| 0.256 | 1600 | 0.0007 | - |
|
| 195 |
+
| 0.264 | 1650 | 0.0004 | - |
|
| 196 |
+
| 0.272 | 1700 | 0.0068 | - |
|
| 197 |
+
| 0.28 | 1750 | 0.0006 | - |
|
| 198 |
+
| 0.288 | 1800 | 0.008 | - |
|
| 199 |
+
| 0.296 | 1850 | 0.0004 | - |
|
| 200 |
+
| 0.304 | 1900 | 0.0009 | - |
|
| 201 |
+
| 0.312 | 1950 | 0.0004 | - |
|
| 202 |
+
| 0.32 | 2000 | 0.0003 | - |
|
| 203 |
+
| 0.328 | 2050 | 0.0034 | - |
|
| 204 |
+
| 0.336 | 2100 | 0.0003 | - |
|
| 205 |
+
| 0.344 | 2150 | 0.0002 | - |
|
| 206 |
+
| 0.352 | 2200 | 0.0002 | - |
|
| 207 |
+
| 0.36 | 2250 | 0.0002 | - |
|
| 208 |
+
| 0.368 | 2300 | 0.0002 | - |
|
| 209 |
+
| 0.376 | 2350 | 0.0002 | - |
|
| 210 |
+
| 0.384 | 2400 | 0.0002 | - |
|
| 211 |
+
| 0.392 | 2450 | 0.0001 | - |
|
| 212 |
+
| 0.4 | 2500 | 0.0002 | - |
|
| 213 |
+
| 0.408 | 2550 | 0.0001 | - |
|
| 214 |
+
| 0.416 | 2600 | 0.0001 | - |
|
| 215 |
+
| 0.424 | 2650 | 0.0002 | - |
|
| 216 |
+
| 0.432 | 2700 | 0.0001 | - |
|
| 217 |
+
| 0.44 | 2750 | 0.0001 | - |
|
| 218 |
+
| 0.448 | 2800 | 0.0001 | - |
|
| 219 |
+
| 0.456 | 2850 | 0.0003 | - |
|
| 220 |
+
| 0.464 | 2900 | 0.0001 | - |
|
| 221 |
+
| 0.472 | 2950 | 0.0001 | - |
|
| 222 |
+
| 0.48 | 3000 | 0.0004 | - |
|
| 223 |
+
| 0.488 | 3050 | 0.0002 | - |
|
| 224 |
+
| 0.496 | 3100 | 0.0001 | - |
|
| 225 |
+
| 0.504 | 3150 | 0.0003 | - |
|
| 226 |
+
| 0.512 | 3200 | 0.0001 | - |
|
| 227 |
+
| 0.52 | 3250 | 0.0001 | - |
|
| 228 |
+
| 0.528 | 3300 | 0.0002 | - |
|
| 229 |
+
| 0.536 | 3350 | 0.0001 | - |
|
| 230 |
+
| 0.544 | 3400 | 0.0001 | - |
|
| 231 |
+
| 0.552 | 3450 | 0.0001 | - |
|
| 232 |
+
| 0.56 | 3500 | 0.0001 | - |
|
| 233 |
+
| 0.568 | 3550 | 0.0001 | - |
|
| 234 |
+
| 0.576 | 3600 | 0.0001 | - |
|
| 235 |
+
| 0.584 | 3650 | 0.0001 | - |
|
| 236 |
+
| 0.592 | 3700 | 0.0001 | - |
|
| 237 |
+
| 0.6 | 3750 | 0.0 | - |
|
| 238 |
+
| 0.608 | 3800 | 0.0001 | - |
|
| 239 |
+
| 0.616 | 3850 | 0.0001 | - |
|
| 240 |
+
| 0.624 | 3900 | 0.0001 | - |
|
| 241 |
+
| 0.632 | 3950 | 0.0001 | - |
|
| 242 |
+
| 0.64 | 4000 | 0.0003 | - |
|
| 243 |
+
| 0.648 | 4050 | 0.0001 | - |
|
| 244 |
+
| 0.656 | 4100 | 0.0001 | - |
|
| 245 |
+
| 0.664 | 4150 | 0.0001 | - |
|
| 246 |
+
| 0.672 | 4200 | 0.0001 | - |
|
| 247 |
+
| 0.68 | 4250 | 0.0001 | - |
|
| 248 |
+
| 0.688 | 4300 | 0.0001 | - |
|
| 249 |
+
| 0.696 | 4350 | 0.0001 | - |
|
| 250 |
+
| 0.704 | 4400 | 0.0001 | - |
|
| 251 |
+
| 0.712 | 4450 | 0.0001 | - |
|
| 252 |
+
| 0.72 | 4500 | 0.0001 | - |
|
| 253 |
+
| 0.728 | 4550 | 0.0001 | - |
|
| 254 |
+
| 0.736 | 4600 | 0.0001 | - |
|
| 255 |
+
| 0.744 | 4650 | 0.0001 | - |
|
| 256 |
+
| 0.752 | 4700 | 0.0001 | - |
|
| 257 |
+
| 0.76 | 4750 | 0.0001 | - |
|
| 258 |
+
| 0.768 | 4800 | 0.0001 | - |
|
| 259 |
+
| 0.776 | 4850 | 0.0001 | - |
|
| 260 |
+
| 0.784 | 4900 | 0.0001 | - |
|
| 261 |
+
| 0.792 | 4950 | 0.0001 | - |
|
| 262 |
+
| 0.8 | 5000 | 0.0 | - |
|
| 263 |
+
| 0.808 | 5050 | 0.0001 | - |
|
| 264 |
+
| 0.816 | 5100 | 0.0001 | - |
|
| 265 |
+
| 0.824 | 5150 | 0.0001 | - |
|
| 266 |
+
| 0.832 | 5200 | 0.0 | - |
|
| 267 |
+
| 0.84 | 5250 | 0.0001 | - |
|
| 268 |
+
| 0.848 | 5300 | 0.0001 | - |
|
| 269 |
+
| 0.856 | 5350 | 0.0 | - |
|
| 270 |
+
| 0.864 | 5400 | 0.0001 | - |
|
| 271 |
+
| 0.872 | 5450 | 0.0001 | - |
|
| 272 |
+
| 0.88 | 5500 | 0.0001 | - |
|
| 273 |
+
| 0.888 | 5550 | 0.0001 | - |
|
| 274 |
+
| 0.896 | 5600 | 0.0 | - |
|
| 275 |
+
| 0.904 | 5650 | 0.0001 | - |
|
| 276 |
+
| 0.912 | 5700 | 0.0001 | - |
|
| 277 |
+
| 0.92 | 5750 | 0.0001 | - |
|
| 278 |
+
| 0.928 | 5800 | 0.0 | - |
|
| 279 |
+
| 0.936 | 5850 | 0.0 | - |
|
| 280 |
+
| 0.944 | 5900 | 0.0 | - |
|
| 281 |
+
| 0.952 | 5950 | 0.0 | - |
|
| 282 |
+
| 0.96 | 6000 | 0.0 | - |
|
| 283 |
+
| 0.968 | 6050 | 0.0 | - |
|
| 284 |
+
| 0.976 | 6100 | 0.0001 | - |
|
| 285 |
+
| 0.984 | 6150 | 0.0 | - |
|
| 286 |
+
| 0.992 | 6200 | 0.0 | - |
|
| 287 |
+
| **1.0** | **6250** | **0.0** | **0.3546** |
|
| 288 |
+
|
| 289 |
+
* The bold row denotes the saved checkpoint.
|
| 290 |
+
### Framework Versions
|
| 291 |
+
- Python: 3.10.13
|
| 292 |
+
- SetFit: 1.0.3
|
| 293 |
+
- Sentence Transformers: 2.2.2
|
| 294 |
+
- Transformers: 4.36.2
|
| 295 |
+
- PyTorch: 2.1.2+cu121
|
| 296 |
+
- Datasets: 2.16.1
|
| 297 |
+
- Tokenizers: 0.15.0
|
| 298 |
+
|
| 299 |
+
## Citation
|
| 300 |
+
|
| 301 |
+
### BibTeX
|
| 302 |
+
```bibtex
|
| 303 |
+
@article{https://doi.org/10.48550/arxiv.2209.11055,
|
| 304 |
+
doi = {10.48550/ARXIV.2209.11055},
|
| 305 |
+
url = {https://arxiv.org/abs/2209.11055},
|
| 306 |
+
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
|
| 307 |
+
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
| 308 |
+
title = {Efficient Few-Shot Learning Without Prompts},
|
| 309 |
+
publisher = {arXiv},
|
| 310 |
+
year = {2022},
|
| 311 |
+
copyright = {Creative Commons Attribution 4.0 International}
|
| 312 |
+
}
|
| 313 |
+
```
|
| 314 |
+
|
| 315 |
+
<!--
|
| 316 |
+
## Glossary
|
| 317 |
+
|
| 318 |
+
*Clearly define terms in order to be accessible across audiences.*
|
| 319 |
+
-->
|
| 320 |
+
|
| 321 |
+
<!--
|
| 322 |
+
## Model Card Authors
|
| 323 |
+
|
| 324 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 325 |
+
-->
|
| 326 |
+
|
| 327 |
+
<!--
|
| 328 |
+
## Model Card Contact
|
| 329 |
+
|
| 330 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 331 |
+
-->
|
config.json
ADDED
|
@@ -0,0 +1,47 @@
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|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "checkpoints/step_6250/",
|
| 3 |
+
"_num_labels": 5,
|
| 4 |
+
"architectures": [
|
| 5 |
+
"BertModel"
|
| 6 |
+
],
|
| 7 |
+
"attention_probs_dropout_prob": 0.1,
|
| 8 |
+
"classifier_dropout": null,
|
| 9 |
+
"directionality": "bidi",
|
| 10 |
+
"hidden_act": "gelu",
|
| 11 |
+
"hidden_dropout_prob": 0.1,
|
| 12 |
+
"hidden_size": 768,
|
| 13 |
+
"id2label": {
|
| 14 |
+
"0": "LABEL_0",
|
| 15 |
+
"1": "LABEL_1",
|
| 16 |
+
"2": "LABEL_2",
|
| 17 |
+
"3": "LABEL_3",
|
| 18 |
+
"4": "LABEL_4"
|
| 19 |
+
},
|
| 20 |
+
"initializer_range": 0.02,
|
| 21 |
+
"intermediate_size": 3072,
|
| 22 |
+
"label2id": {
|
| 23 |
+
"LABEL_0": 0,
|
| 24 |
+
"LABEL_1": 1,
|
| 25 |
+
"LABEL_2": 2,
|
| 26 |
+
"LABEL_3": 3,
|
| 27 |
+
"LABEL_4": 4
|
| 28 |
+
},
|
| 29 |
+
"layer_norm_eps": 1e-12,
|
| 30 |
+
"max_position_embeddings": 512,
|
| 31 |
+
"model_type": "bert",
|
| 32 |
+
"num_attention_heads": 12,
|
| 33 |
+
"num_hidden_layers": 12,
|
| 34 |
+
"output_past": true,
|
| 35 |
+
"pad_token_id": 0,
|
| 36 |
+
"pooler_fc_size": 768,
|
| 37 |
+
"pooler_num_attention_heads": 12,
|
| 38 |
+
"pooler_num_fc_layers": 3,
|
| 39 |
+
"pooler_size_per_head": 128,
|
| 40 |
+
"pooler_type": "first_token_transform",
|
| 41 |
+
"position_embedding_type": "absolute",
|
| 42 |
+
"torch_dtype": "float32",
|
| 43 |
+
"transformers_version": "4.36.2",
|
| 44 |
+
"type_vocab_size": 2,
|
| 45 |
+
"use_cache": true,
|
| 46 |
+
"vocab_size": 50000
|
| 47 |
+
}
|
config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"__version__": {
|
| 3 |
+
"sentence_transformers": "2.2.2",
|
| 4 |
+
"transformers": "4.20.1",
|
| 5 |
+
"pytorch": "1.11.0"
|
| 6 |
+
}
|
| 7 |
+
}
|
config_setfit.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"labels": [
|
| 3 |
+
"sangat negatif",
|
| 4 |
+
"negatif",
|
| 5 |
+
"netral",
|
| 6 |
+
"positif",
|
| 7 |
+
"sangat positif"
|
| 8 |
+
],
|
| 9 |
+
"normalize_embeddings": false
|
| 10 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0d2a8f42ec04b816aaf02e1a21e70d2eb0b68871bdceaa58c7c07a9c72d1ead1
|
| 3 |
+
size 497787752
|
model_head.pkl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4c4cc1a3430422fff821dbd9e1f63b23e94091b0d597bc104c7a48fa18920aba
|
| 3 |
+
size 16912
|
modules.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"idx": 0,
|
| 4 |
+
"name": "0",
|
| 5 |
+
"path": "",
|
| 6 |
+
"type": "sentence_transformers.models.Transformer"
|
| 7 |
+
},
|
| 8 |
+
{
|
| 9 |
+
"idx": 1,
|
| 10 |
+
"name": "1",
|
| 11 |
+
"path": "1_Pooling",
|
| 12 |
+
"type": "sentence_transformers.models.Pooling"
|
| 13 |
+
}
|
| 14 |
+
]
|
sentence_bert_config.json
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"max_seq_length": 512,
|
| 3 |
+
"do_lower_case": false
|
| 4 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cls_token": {
|
| 3 |
+
"content": "[CLS]",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"mask_token": {
|
| 10 |
+
"content": "[MASK]",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": {
|
| 17 |
+
"content": "[PAD]",
|
| 18 |
+
"lstrip": false,
|
| 19 |
+
"normalized": false,
|
| 20 |
+
"rstrip": false,
|
| 21 |
+
"single_word": false
|
| 22 |
+
},
|
| 23 |
+
"sep_token": {
|
| 24 |
+
"content": "[SEP]",
|
| 25 |
+
"lstrip": false,
|
| 26 |
+
"normalized": false,
|
| 27 |
+
"rstrip": false,
|
| 28 |
+
"single_word": false
|
| 29 |
+
},
|
| 30 |
+
"unk_token": {
|
| 31 |
+
"content": "[UNK]",
|
| 32 |
+
"lstrip": false,
|
| 33 |
+
"normalized": false,
|
| 34 |
+
"rstrip": false,
|
| 35 |
+
"single_word": false
|
| 36 |
+
}
|
| 37 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,64 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "[PAD]",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"1": {
|
| 12 |
+
"content": "[UNK]",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"2": {
|
| 20 |
+
"content": "[CLS]",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"3": {
|
| 28 |
+
"content": "[SEP]",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"4": {
|
| 36 |
+
"content": "[MASK]",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
}
|
| 43 |
+
},
|
| 44 |
+
"clean_up_tokenization_spaces": true,
|
| 45 |
+
"cls_token": "[CLS]",
|
| 46 |
+
"do_basic_tokenize": true,
|
| 47 |
+
"do_lower_case": true,
|
| 48 |
+
"mask_token": "[MASK]",
|
| 49 |
+
"max_length": 512,
|
| 50 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 51 |
+
"never_split": null,
|
| 52 |
+
"pad_to_multiple_of": null,
|
| 53 |
+
"pad_token": "[PAD]",
|
| 54 |
+
"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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|
|