Add SetFit ABSA model

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "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

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+---
+library_name: setfit
+tags:
+- setfit
+- absa
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: level:game bagus banget sumpah udah nyelesain level 1 sampe level 8 gak sengaja
+    kehapus download save level 1 level 2 level 3 sampe level 8 gak save
+- text: update:game nya bagus sih 1 bug error bermain geometry nya meloncat loncat
+    tau wi fi potato kasih game robtop 4 bintang semoga update diperbaiki d
+- text: lagu:game nya bgs seru game nya gk susah pake offline cmn 1 kekurangannya
+    gk game trs gk ganti lagu jd nya dimatiin lgu dri nya trs pake lagu sekian ulasan
+    terima kasih
+- text: kali:game nya seru kali mainin muncul iklan mohon ya iklannya dikurangin yg
+    install sabar ya main nya susah
+- text: kekurangannya:game nya bgs seru game nya gk susah pake offline cmn 1 kekurangannya
+    gk game trs gk ganti lagu jd nya dimatiin lgu dri nya trs pake lagu sekian ulasan
+    terima kasih
+pipeline_tag: text-classification
+inference: false
+---
+
+# SetFit Aspect 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 filtering aspect span candidates.
+
+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 this SetFit model to filter these possible aspect span candidates.**
+3. Use a 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:** [jetri20/ABSA_review_game_geometry-aspect](https://huggingface.co/jetri20/ABSA_review_game_geometry-aspect)
+- **SetFitABSA Polarity Model:** [jetri20/ABSA_review_game_geometry-polarity](https://huggingface.co/jetri20/ABSA_review_game_geometry-polarity)
+- **Maximum Sequence Length:** 512 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+|:----------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| aspect    | <ul><li>'level:sih game level nya sulit banget level clutter funk susah nya ampun level 11 nya level sulit nya level 10 sulit menyerah sabar banting hp saking sulit nya ya geometri dash kebanyakan level nya sulit mah geometri dash game stress saking susah nya'</li><li>'iklan:iklan emang sih level iklan muncul masuk level mending kayak mah'</li><li>'game:game apasih sampe strees gitu final boss level the tower susahnya ampun kenapasih kalo hijau pas udah nya jatuh mati sih cube nya cuman 1 hp ya ngeselin sih'</li></ul>                                                                                                                                                                                                                                                                                                    |
+| no aspect | <ul><li>'sih game level:sih game level nya sulit banget level clutter funk susah nya ampun level 11 nya level sulit nya level 10 sulit menyerah sabar banting hp saking sulit nya ya geometri dash kebanyakan level nya sulit mah geometri dash game stress saking susah nya'</li><li>'level clutter funk:sih game level nya sulit banget level clutter funk susah nya ampun level 11 nya level sulit nya level 10 sulit menyerah sabar banting hp saking sulit nya ya geometri dash kebanyakan level nya sulit mah geometri dash game stress saking susah nya'</li><li>'level:sih game level nya sulit banget level clutter funk susah nya ampun level 11 nya level sulit nya level 10 sulit menyerah sabar banting hp saking sulit nya ya geometri dash kebanyakan level nya sulit mah geometri dash game stress saking susah nya'</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(
+    "jetri20/ABSA_review_game_geometry-aspect",
+    "jetri20/ABSA_review_game_geometry-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   | 2   | 23.5963 | 67  |
+
+| Label     | Training Sample Count |
+|:----------|:----------------------|
+| no aspect | 754                   |
+| aspect    | 321                   |
+
+### Training Hyperparameters
+- batch_size: (4, 4)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 5
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0004 | 1    | 0.3713        | -               |
+| 0.0186 | 50   | 0.2045        | -               |
+| 0.0372 | 100  | 0.1548        | -               |
+| 0.0558 | 150  | 0.3116        | -               |
+| 0.0744 | 200  | 0.2066        | -               |
+| 0.0930 | 250  | 0.2932        | -               |
+| 0.1116 | 300  | 0.3138        | -               |
+| 0.1302 | 350  | 0.1258        | -               |
+| 0.1488 | 400  | 0.3442        | -               |
+| 0.1674 | 450  | 0.0558        | -               |
+| 0.1860 | 500  | 0.2819        | -               |
+| 0.2046 | 550  | 0.2211        | -               |
+| 0.2232 | 600  | 0.1269        | -               |
+| 0.2418 | 650  | 0.0098        | -               |
+| 0.2604 | 700  | 0.2395        | -               |
+| 0.2790 | 750  | 0.4382        | -               |
+| 0.2976 | 800  | 0.488         | -               |
+| 0.3162 | 850  | 0.6662        | -               |
+| 0.3348 | 900  | 0.1811        | -               |
+| 0.3534 | 950  | 0.2431        | -               |
+| 0.3720 | 1000 | 0.2032        | -               |
+| 0.3906 | 1050 | 0.0475        | -               |
+| 0.4092 | 1100 | 0.177         | -               |
+| 0.4278 | 1150 | 0.0556        | -               |
+| 0.4464 | 1200 | 0.3048        | -               |
+| 0.4650 | 1250 | 0.0015        | -               |
+| 0.4836 | 1300 | 0.0841        | -               |
+| 0.5022 | 1350 | 0.0105        | -               |
+| 0.5208 | 1400 | 0.0036        | -               |
+| 0.5394 | 1450 | 0.2296        | -               |
+| 0.5580 | 1500 | 0.0045        | -               |
+| 0.5766 | 1550 | 0.0134        | -               |
+| 0.5952 | 1600 | 0.0367        | -               |
+| 0.6138 | 1650 | 0.0044        | -               |
+| 0.6324 | 1700 | 0.0068        | -               |
+| 0.6510 | 1750 | 0.1408        | -               |
+| 0.6696 | 1800 | 0.0092        | -               |
+| 0.6882 | 1850 | 0.1926        | -               |
+| 0.7068 | 1900 | 0.0014        | -               |
+| 0.7254 | 1950 | 0.0003        | -               |
+| 0.7440 | 2000 | 0.2094        | -               |
+| 0.7626 | 2050 | 0.0329        | -               |
+| 0.7812 | 2100 | 0.0028        | -               |
+| 0.7999 | 2150 | 0.0144        | -               |
+| 0.8185 | 2200 | 0.1555        | -               |
+| 0.8371 | 2250 | 0.0005        | -               |
+| 0.8557 | 2300 | 0.0067        | -               |
+| 0.8743 | 2350 | 0.1485        | -               |
+| 0.8929 | 2400 | 0.0034        | -               |
+| 0.9115 | 2450 | 0.0044        | -               |
+| 0.9301 | 2500 | 0.2752        | -               |
+| 0.9487 | 2550 | 0.1342        | -               |
+| 0.9673 | 2600 | 0.0108        | -               |
+| 0.9859 | 2650 | 0.0106        | -               |
+| 1.0    | 2688 | -             | 0.2236          |
+
+### 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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+{
+  "_name_or_path": "firqaaa/indo-setfit-absa-bert-base-restaurants-aspect",
+  "_num_labels": 5,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50000
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.36.2",
+    "pytorch": "2.1.2"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+{
+  "labels": [
+    "no aspect",
+    "aspect"
+  ],
+  "span_context": 0,
+  "spacy_model": "id_core_news_trf",
+  "normalize_embeddings": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1b89ea466602ba0f5c2c11074cd4475b749d3b3cf42d39a6c16d515df28e11d4
+size 497787752

model_head.pkl

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+oid sha256:d5de6040ed919d10d03acb453e580f233eb9923b6cc853a823b8fc1769f93274
+size 6991

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}