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 +1,233 @@
+---
+library_name: setfit
+tags:
+- setfit
+- absa
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+base_model: firqaaa/indo-setfit-absa-bert-base-restaurants-polarity
+metrics:
+- accuracy
+widget:
+- text: game sebenernya bagus storynya mapnya grafis pemandangan:game sebenernya bagus
+    storynya mapnya grafis pemandangan alamnya bagus kesalahan game sistem farmingnya
+    yg bikin frustasi player grindingnya bikin pusing yg lakuin ya sih maaf ya game
+    ga sebagus
+- text: story grafik kecewa reward player gak berubah:game nya bagus story grafik
+    kecewa reward player gak berubah rilis nambah sih apresiasi player bermain game
+    contoh nya 3x pull tarikan gacha mengenang 3 bermain game jujur mengecewakan player
+    gak anggap download pikir karna game kikir
+- text: hoyoverse ngurusin ni game seru d:game debes yg gwe temuin ampe gameplay seru
+    story mantul map luas bgt grapik salutlah ama hoyoverse coba klo hoyoverse ngurusin
+    ni game seru d
+- text: penggunaan data terlalau besarr anjj:penggunaan data terlalau besarr anjj
+    sekalii
+- text: story rate 8 10:story rate 8 10 permainan yng bagus gacha bansos 10 100
+pipeline_tag: text-classification
+inference: false
+---
+
+# SetFit Polarity Model with firqaaa/indo-setfit-absa-bert-base-restaurants-polarity
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [firqaaa/indo-setfit-absa-bert-base-restaurants-polarity](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-polarity) 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. 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 body:** [firqaaa/indo-setfit-absa-bert-base-restaurants-polarity](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-polarity)
+- **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:** [Funnyworld1412/ABSA_review_game_genshin_impact-aspect](https://huggingface.co/Funnyworld1412/ABSA_review_game_genshin_impact-aspect)
+- **SetFitABSA Polarity Model:** [Funnyworld1412/ABSA_review_game_genshin_impact-polarity](https://huggingface.co/Funnyworld1412/ABSA_review_game_genshin_impact-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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+|:--------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| negatif | <ul><li>'kebanyakan npc teyvat story utama mc dilupain:saranku developer menciptakan story menarik kehilangan player player yg bertahan repetitif monoton update size gede doang yg isinya chest itupun sampah puzzle yg rumit chest nya sampah story kebanyakan npc teyvat story utama mc dilupain gak difokusin map kalo udah kosong ya nyampah bikin size gede doang main 3 monoton perkembangan buruk'</li><li>'tolong ditambah reward gachanya player kesulitan:tolong ditambah reward gachanya player kesulitan primo quest eksplorasi 100 dasar developer kapitalis game monoton ramah player kekurangan bahan gacha karakter'</li><li>'gitu aja sampek event selesai primogemnya 10:cuman saran pelit biar player gak kabur game sebelah hadiah event quest perbaiki udah nunggu event hadiah cuman gitu gitu aja sampek event selesai primogemnya 10 pull gacha gak tingakat kesulitan beda hadiah main kabur kalok pelit 1 jariang mohon perbaiki server indonya trimaksih'</li></ul>                                                                                     |
+| positif | <ul><li>'gameplay nya menarik story:gameplay nya menarik story questnya bikin boring menyelesaikan quest kepala frustasi karna dialog gak ngotak panjangnya gak skip developer pelit hadiah senang bermain game karna kikirannya puzzle nya questnya otak pusing developer respon bug tunggu viral bug nya benerin'</li><li>'selebihnya bagus cerita story sound effect tampilan:tolong pelit hoyoverse pemain pemain yg yg main karna pemain suka gratisan ntah artefak primoge character item karna jujur pemain puas gamenya upgrade character kumpulan item yg kebanyakan susah pemain kekurangan game selebihnya bagus cerita story sound effect tampilan didalam game yg lumayan bagus'</li><li>'bagus cerita story sound effect tampilan didalam game:tolong pelit hoyoverse pemain pemain yg yg main karna pemain suka gratisan ntah artefak primoge character item karna jujur pemain puas gamenya upgrade character kumpulan item yg kebanyakan susah pemain kekurangan game selebihnya bagus cerita story sound effect tampilan didalam game yg lumayan bagus'</li></ul> |
+
+## 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(
+    "Funnyworld1412/ABSA_review_game_genshin_impact-aspect",
+    "Funnyworld1412/ABSA_review_game_genshin_impact-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   | 31.0185 | 70  |
+
+| Label   | Training Sample Count |
+|:--------|:----------------------|
+| konflik | 0                     |
+| negatif | 208                   |
+| netral  | 0                     |
+| positif | 116                   |
+
+### Training Hyperparameters
+- batch_size: (4, 4)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 10
+- 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: False
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0006 | 1    | 0.2317        | -               |
+| 0.0309 | 50   | 0.0253        | -               |
+| 0.0617 | 100  | 0.0008        | -               |
+| 0.0926 | 150  | 0.4789        | -               |
+| 0.1235 | 200  | 0.0215        | -               |
+| 0.1543 | 250  | 0.0012        | -               |
+| 0.1852 | 300  | 0.1843        | -               |
+| 0.2160 | 350  | 0.0014        | -               |
+| 0.2469 | 400  | 0.0013        | -               |
+| 0.2778 | 450  | 0.0012        | -               |
+| 0.3086 | 500  | 0.0016        | -               |
+| 0.3395 | 550  | 0.0004        | -               |
+| 0.3704 | 600  | 0.0006        | -               |
+| 0.4012 | 650  | 0.0017        | -               |
+| 0.4321 | 700  | 0.0012        | -               |
+| 0.4630 | 750  | 0.0005        | -               |
+| 0.4938 | 800  | 0.0003        | -               |
+| 0.5247 | 850  | 0.0004        | -               |
+| 0.5556 | 900  | 0.0006        | -               |
+| 0.5864 | 950  | 0.2368        | -               |
+| 0.6173 | 1000 | 0.0003        | -               |
+| 0.6481 | 1050 | 0.0005        | -               |
+| 0.6790 | 1100 | 0.0006        | -               |
+| 0.7099 | 1150 | 0.0008        | -               |
+| 0.7407 | 1200 | 0.0924        | -               |
+| 0.7716 | 1250 | 0.0003        | -               |
+| 0.8025 | 1300 | 0.0003        | -               |
+| 0.8333 | 1350 | 0.0003        | -               |
+| 0.8642 | 1400 | 0.0006        | -               |
+| 0.8951 | 1450 | 0.0005        | -               |
+| 0.9259 | 1500 | 0.0004        | -               |
+| 0.9568 | 1550 | 0.0003        | -               |
+| 0.9877 | 1600 | 0.0002        | -               |
+| 1.0    | 1620 | -             | 0.1328          |
+
+### Framework Versions
+- Python: 3.10.13
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.1
+- spaCy: 3.7.5
+- Transformers: 4.36.2
+- PyTorch: 2.1.2
+- Datasets: 2.19.2
+- 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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+}

config_sentence_transformers.json

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+  "similarity_fn_name": null
+}

config_setfit.json

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

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+}