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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+{
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+  "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 +1,227 @@
+---
+library_name: setfit
+tags:
+- setfit
+- absa
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: yg sama. Rasanya konsisten dari dulu:Kalo ke Bandung, wajib banget nyobain
+    makan siang disini. Tempatnya selalu ramee walau cabangnya ada bbrp di 1 jalan
+    yg sama. Rasanya konsisten dari dulu mah, enakkk! Ayam bakar sama sayur asem wajib
+    dipesen. Dan sambelnya yg selalu juara pedesnya, siap2 keringetan
+- text: jam lebih dan tempatnya panas. Makanannya:Di satu deretan ada 3 warung bu
+    imas dan rame semua Nunggu makan dateng sekitar 1 jam lebih dan tempatnya panas.
+    Makanannya sebenarnya enak2 semua tapi kalo harus antri lama dan temptnya kurang
+    oke mending cari warung makan sunda lain
+- text: Dari makanan yang luar biasa:Dari makanan yang luar biasa, hingga suasana
+    yang hangat, hingga layanan yang ramah, tempat lingkungan pusat kota ini tidak
+    ketinggalan.
+- text: Favorite sambal terasi dadak di Bandung sejauh:Favorite sambal terasi dadak
+    di Bandung sejauh ini Harganya pun ramah. Next time balik lagi.
+- text: ayam goreng/ati-ampela goreng gurih asinnya pas:Rasa ayam goreng/ati-ampela
+    goreng gurih asinnya pas, sayur asem yang isinya banyak dan ras asam-manisnya
+    nyambung, dan sambal leunca-nya enak beutullll.... Pakai petai dan tempe/tahu
+    lebih sempurna.
+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: 0.8636363636363636
+      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:** [pahri/setfit-indo-resto-RM-ibu-imas-aspect](https://huggingface.co/pahri/setfit-indo-resto-RM-ibu-imas-aspect)
+- **SetFitABSA Polarity Model:** [pahri/setfit-indo-resto-RM-ibu-imas-polarity](https://huggingface.co/pahri/setfit-indo-resto-RM-ibu-imas-polarity)
+- **Maximum Sequence Length:** 8192 tokens
+- **Number of Classes:** 2 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
+|:---------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| positive | <ul><li>'air krispi dan ayam bakar:Warung Sunda murah meriah dan makanannya enak. Favorit selada air krispi dan ayam bakar'</li><li>'Ayam bakar,sambel leunca:Ayam bakar,sambel leunca sambel terasi merah enak banget 9/10, perkedel jagung 8/10 makan pakai sambel mantap. Makan berdua sekitar 77k'</li><li>',sambel leunca sambel terasi merah enak banget 9:Ayam bakar,sambel leunca sambel terasi merah enak banget 9/10, perkedel jagung 8/10 makan pakai sambel mantap. Makan berdua sekitar 77k'</li></ul>                                                                                              |
+| negative | <ul><li>', minus di menu tidak di cantumkan:Makanan biasa saja, minus di menu tidak di cantumkan harga. Posi nasi standar, kelebihan sambal sudah disediakan di mangkok. '</li><li>'lebih diatur kah antriannya, kayanya pakai:It wasnt bad food at all. Tapi please mungkin bisa lebih diatur kah antriannya, kayanya pakai waiting list gak sesulit itu deh.'</li><li>'rasanya standar. Harga bisa dibilang murah:Tahu tempe perkedel rasanya standar. Harga bisa dibilang murah. Kalau yang masih penasaran ya boleh dateng coba tapi menurut saya overall biasa saja, tidak nemu wah nya dimana..'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8636   |
+
+## 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(
+    "pahri/setfit-indo-resto-RM-ibu-imas-aspect",
+    "pahri/setfit-indo-resto-RM-ibu-imas-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   | 7   | 35.3922 | 90  |
+
+| Label   | Training Sample Count |
+|:--------|:----------------------|
+| konflik | 0                     |
+| negatif | 0                     |
+| netral  | 0                     |
+| positif | 0                     |
+
+### 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0036 | 1    | 0.2676        | -               |
+| 0.1799 | 50   | 0.0064        | -               |
+| 0.3597 | 100  | 0.0015        | -               |
+| 0.5396 | 150  | 0.0007        | -               |
+| 0.7194 | 200  | 0.0005        | -               |
+| 0.8993 | 250  | 0.0006        | -               |
+
+### 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"
+  ],
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+  "hidden_act": "gelu",
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+  "hidden_size": 1024,
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+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
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+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+    "sentence_transformers": "2.2.2",
+    "transformers": "4.33.0",
+    "pytorch": "2.1.2+cu121"
+  },
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+}

config_setfit.json

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

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modules.json

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sentence_bert_config.json

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special_tokens_map.json

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tokenizer_config.json

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