Add SetFit ABSA model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,291 @@
+---
+library_name: setfit
+tags:
+- setfit
+- absa
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: dan lembut, pai yang dibawa pulang menjadi basah di:Karena kulitnya yang tipis
+    dan lembut, pai yang dibawa pulang menjadi basah di dalam kotaknya.
+- text: mungkin untuk mengkritik makanannya tersebut.:Dari makanan pembuka yang kami
+    makan, dim sum, dan variasi makanannya lainnya, tidak mungkin untuk mengkritik
+    makanannya tersebut.
+- text: di sana untuk Spesial Sabtu Malam Setengah Harga, tetapi Selasa:Saya tidak
+    ada di sana untuk Spesial Sabtu Malam Setengah Harga, tetapi Selasa Malam.
+- text: dan mengatur ulang meja untuk enam orang:Di sebelah kanan saya, nyonya rumah
+    berdiri di dekat seorang busboy dan mendesiskan rapido, rapido ketika dia mencoba
+    membersihkan dan mengatur ulang meja untuk enam orang.
+- text: Jika Anda menyukai makanannya dan nilai yang:Jika Anda menyukai makanannya
+    dan nilai yang Anda dapatkan dari beberapa restoran Chinatown, ini bukan tempat
+    untuk Anda.
+pipeline_tag: text-classification
+inference: false
+model-index:
+- name: SetFit Polarity Model
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.6568627450980392
+      name: Accuracy
+---
+
+# SetFit Polarity Model
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of classifying aspect polarities.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+This model was trained within the context of a larger system for ABSA, which looks like so:
+
+1. Use a spaCy model to select possible aspect span candidates.
+2. Use a SetFit model to filter these possible aspect span candidates.
+3. **Use this SetFit model to classify the filtered aspect span candidates.**
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **spaCy Model:** id_core_news_trf
+- **SetFitABSA Aspect Model:** [zeroix07/indo-setfit-absa-model-aspect](https://huggingface.co/zeroix07/indo-setfit-absa-model-aspect)
+- **SetFitABSA Polarity Model:** [zeroix07/indo-setfit-absa-model-polarity](https://huggingface.co/zeroix07/indo-setfit-absa-model-polarity)
+- **Maximum Sequence Length:** 8192 tokens
+- **Number of Classes:** 3 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label   | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+|:--------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| positif | <ul><li>'faktor penebusan adalah makanannya, yang berada:Agar benar-benar adil, satu-satunya faktor penebusan adalah makanannya, yang berada di atas rata-rata, tetapi tidak dapat menutupi semua kekurangan Teodora lainnya.'</li><li>'makanannya benar-benar luar biasa:makanannya benar-benar luar biasa, dengan dapur yang sangat mumpuni yang dengan bangga akan menyiapkan apa pun yang Anda ingin makan, baik itu ada di menu atau tidak.'</li><li>'biasa, dengan dapur yang sangat mumpuni:makanannya benar-benar luar biasa, dengan dapur yang sangat mumpuni yang dengan bangga akan menyiapkan apa pun yang Anda ingin makan, baik itu ada di menu atau tidak.'</li></ul> |
+| netral  | <ul><li>'itu ada di menu atau tidak.:makanannya benar-benar luar biasa, dengan dapur yang sangat mumpuni yang dengan bangga akan menyiapkan apa pun yang Anda ingin makan, baik itu ada di menu atau tidak.'</li><li>'bisa mencicipi kedua daging tersebut).:Favorit kami yang disepakati adalah orrechiete dengan sosis dan ayam (biasanya para pelayan berbaik hati membagi hidangan menjadi dua sehingga Anda bisa mencicipi kedua daging tersebut).'</li><li>'jika Anda suka pizza berkulit tipis.:Pizza adalah yang terbaik jika Anda suka pizza berkulit tipis.'</li></ul>                                                                                                     |
+| negatif | <ul><li>'yang masuk ke koki.:Semua uang digunakan untuk dekorasi interior, tidak ada satupun yang masuk ke koki.'</li><li>'masuk akal meskipun layanannya buruk.:Harganya masuk akal meskipun layanannya buruk.'</li><li>'mayones, lupa roti panggang kami, meninggalkan:Mereka tidak memiliki mayones, lupa roti panggang kami, meninggalkan bahan-bahan (yaitu keju dalam telur dadar), di bawah suhu panas dan daging terlalu matang sehingga hancur di piring ketika Anda menyentuhnya.'</li></ul>                                                                                                                                                                               |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.6569   |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import AbsaModel
+
+# Download from the 🤗 Hub
+model = AbsaModel.from_pretrained(
+    "zeroix07/indo-setfit-absa-model-aspect",
+    "zeroix07/indo-setfit-absa-model-polarity",
+)
+# Run inference
+preds = model("The food was great, but the venue is just way too busy.")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median  | Max |
+|:-------------|:----|:--------|:----|
+| Word count   | 5   | 21.6519 | 45  |
+
+| Label   | Training Sample Count |
+|:--------|:----------------------|
+| konflik | 0                     |
+| negatif | 48                    |
+| netral  | 69                    |
+| positif | 64                    |
+
+### Training Hyperparameters
+- batch_size: (6, 6)
+- num_epochs: (1, 16)
+- max_steps: -1
+- sampling_strategy: oversampling
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: True
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0003 | 1    | 0.2985        | -               |
+| 0.0139 | 50   | 0.14          | -               |
+| 0.0278 | 100  | 0.0913        | -               |
+| 0.0417 | 150  | 0.0447        | -               |
+| 0.0556 | 200  | 0.0932        | -               |
+| 0.0694 | 250  | 0.2864        | -               |
+| 0.0833 | 300  | 0.2556        | -               |
+| 0.0972 | 350  | 0.1447        | -               |
+| 0.1111 | 400  | 0.0084        | -               |
+| 0.125  | 450  | 0.003         | -               |
+| 0.1389 | 500  | 0.0035        | -               |
+| 0.1528 | 550  | 0.0074        | -               |
+| 0.1667 | 600  | 0.0031        | -               |
+| 0.1806 | 650  | 0.0014        | -               |
+| 0.1944 | 700  | 0.002         | -               |
+| 0.2083 | 750  | 0.0006        | -               |
+| 0.2222 | 800  | 0.0005        | -               |
+| 0.2361 | 850  | 0.0005        | -               |
+| 0.25   | 900  | 0.0005        | -               |
+| 0.2639 | 950  | 0.0015        | -               |
+| 0.2778 | 1000 | 0.0007        | -               |
+| 0.2917 | 1050 | 0.0006        | -               |
+| 0.3056 | 1100 | 0.0006        | -               |
+| 0.3194 | 1150 | 0.0007        | -               |
+| 0.3333 | 1200 | 0.0091        | -               |
+| 0.3472 | 1250 | 0.0004        | -               |
+| 0.3611 | 1300 | 0.0003        | -               |
+| 0.375  | 1350 | 0.0005        | -               |
+| 0.3889 | 1400 | 0.0006        | -               |
+| 0.4028 | 1450 | 0.0434        | -               |
+| 0.4167 | 1500 | 0.0006        | -               |
+| 0.4306 | 1550 | 0.0003        | -               |
+| 0.4444 | 1600 | 0.0005        | -               |
+| 0.4583 | 1650 | 0.0004        | -               |
+| 0.4722 | 1700 | 0.0021        | -               |
+| 0.4861 | 1750 | 0.0012        | -               |
+| 0.5    | 1800 | 0.0004        | -               |
+| 0.5139 | 1850 | 0.0005        | -               |
+| 0.5278 | 1900 | 0.0004        | -               |
+| 0.5417 | 1950 | 0.0003        | -               |
+| 0.5556 | 2000 | 0.0003        | -               |
+| 0.5694 | 2050 | 0.0005        | -               |
+| 0.5833 | 2100 | 0.0004        | -               |
+| 0.5972 | 2150 | 0.0004        | -               |
+| 0.6111 | 2200 | 0.0005        | -               |
+| 0.625  | 2250 | 0.0004        | -               |
+| 0.6389 | 2300 | 0.0005        | -               |
+| 0.6528 | 2350 | 0.0004        | -               |
+| 0.6667 | 2400 | 0.0003        | -               |
+| 0.6806 | 2450 | 0.0004        | -               |
+| 0.6944 | 2500 | 0.0007        | -               |
+| 0.7083 | 2550 | 0.0003        | -               |
+| 0.7222 | 2600 | 0.0003        | -               |
+| 0.7361 | 2650 | 0.101         | -               |
+| 0.75   | 2700 | 0.0003        | -               |
+| 0.7639 | 2750 | 0.0004        | -               |
+| 0.7778 | 2800 | 0.0004        | -               |
+| 0.7917 | 2850 | 0.0003        | -               |
+| 0.8056 | 2900 | 0.0004        | -               |
+| 0.8194 | 2950 | 0.0899        | -               |
+| 0.8333 | 3000 | 0.0003        | -               |
+| 0.8472 | 3050 | 0.0002        | -               |
+| 0.8611 | 3100 | 0.0002        | -               |
+| 0.875  | 3150 | 0.0003        | -               |
+| 0.8889 | 3200 | 0.0002        | -               |
+| 0.9028 | 3250 | 0.0003        | -               |
+| 0.9167 | 3300 | 0.0004        | -               |
+| 0.9306 | 3350 | 0.0003        | -               |
+| 0.9444 | 3400 | 0.0003        | -               |
+| 0.9583 | 3450 | 0.0547        | -               |
+| 0.9722 | 3500 | 0.0003        | -               |
+| 0.9861 | 3550 | 0.0004        | -               |
+| 1.0    | 3600 | 0.0002        | -               |
+
+### Framework Versions
+- Python: 3.10.13
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- spaCy: 3.7.4
+- Transformers: 4.36.2
+- PyTorch: 2.1.2
+- Datasets: 2.18.0
+- Tokenizers: 0.15.2
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "_name_or_path": "firqaaa/indo-setfit-absa-bert-base-restaurants-polarity",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
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+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
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+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 24,
+  "output_past": true,
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+  "use_cache": true,
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+}

config_sentence_transformers.json

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+    "sentence_transformers": "2.2.2",
+    "transformers": "4.33.0",
+    "pytorch": "2.1.2+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

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+{
+  "labels": [
+    "konflik",
+    "negatif",
+    "netral",
+    "positif"
+  ],
+  "normalize_embeddings": false,
+  "spacy_model": "id_core_news_trf",
+  "span_context": 3
+}

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modules.json

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+  }
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sentence_bert_config.json

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+}

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special_tokens_map.json

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tokenizer_config.json

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+}