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zeroix07
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indo-setfit-absa-model-aspect

Text Classification
setfit
Safetensors
sentence-transformers
bert
absa
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
 Community
zeroix07 commited on
Commit
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Add SetFit ABSA model

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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +899 -0
  3. config.json +47 -0
  4. config_sentence_transformers.json +9 -0
  5. config_setfit.json +9 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +64 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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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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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ library_name: setfit
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+ tags:
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+ - setfit
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+ - absa
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: sashimi:Kami berbagi sebotol sake, pesanan edamame, dan dia makan sesepiring
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+ sushi sementara saya makan sashimi.
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+ - text: kelompok:tempat agak kecil tapi saya kira jika mereka tidak terlalu sibuk
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+ mungkin bisa memuat kelompok atau anak-anak.
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+ - text: Suan:Lokasinya yang bagus dan fakta bahwa Hutner College dekat serta harga
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+ sangat masuk akal, membuat siswa kembali ke Suan lagi dan lagi.
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+ - text: rapido:Di sebelah kanan saya, nyonya rumah berdiri di dekat seorang busboy
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+ dan mendesiskan rapido, rapido ketika dia mencoba membersihkan dan mengatur ulang
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+ meja untuk enam orang.
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+ - text: hidangan:Jangan bersantap di Tamarind untuk hidangan vegetarian, mereka tidak
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+ setara dengan pilihan non-sayuran.
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+ pipeline_tag: text-classification
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+ inference: false
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+ model-index:
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+ - name: SetFit Aspect Model
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.7836879432624113
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+ name: Accuracy
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+ ---
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+
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+ # SetFit Aspect Model
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+
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+ 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.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ This model was trained within the context of a larger system for ABSA, which looks like so:
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+
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+ 1. Use a spaCy model to select possible aspect span candidates.
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+ 2. **Use this SetFit model to filter these possible aspect span candidates.**
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+ 3. Use a SetFit model to classify the filtered aspect span candidates.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ <!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **spaCy Model:** id_core_news_trf
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+ - **SetFitABSA Aspect Model:** [zeroix07/indo-setfit-absa-model-aspect](https://huggingface.co/zeroix07/indo-setfit-absa-model-aspect)
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+ - **SetFitABSA Polarity Model:** [zeroix07/indo-setfit-absa-model-polarity](https://huggingface.co/zeroix07/indo-setfit-absa-model-polarity)
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 2 classes
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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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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | aspect | <ul><li>'makanannya: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: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>'dapur: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> |
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+ | no aspect | <ul><li>'faktor penebusan: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>'atas: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>'kekurangan Teodora: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></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.7837 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import AbsaModel
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+
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+ # Download from the 🤗 Hub
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+ model = AbsaModel.from_pretrained(
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+ "zeroix07/indo-setfit-absa-model-aspect",
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+ "zeroix07/indo-setfit-absa-model-polarity",
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+ )
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+ # Run inference
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+ preds = model("The food was great, but the venue is just way too busy.")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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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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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:--------|:----|
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+ | Word count | 2 | 17.4396 | 40 |
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+
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+ | Label | Training Sample Count |
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+ |:----------|:----------------------|
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+ | no aspect | 415 |
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+ | aspect | 181 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (6, 6)
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+ - num_epochs: (1, 16)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: True
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+ - warmup_proportion: 0.1
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:-----:|:-------------:|:---------------:|
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+ | 0.0000 | 1 | 0.2509 | - |
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+ | 0.0015 | 50 | 0.1002 | - |
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+ | 0.0029 | 100 | 0.2166 | - |
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+ | 0.0044 | 150 | 0.1083 | - |
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+ | 0.0058 | 200 | 0.2008 | - |
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+ | 0.0073 | 250 | 0.2292 | - |
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+ | 0.0088 | 300 | 0.1745 | - |
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+ | 0.0102 | 350 | 0.207 | - |
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+ | 0.0117 | 400 | 0.0432 | - |
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+ | 0.0131 | 450 | 0.0122 | - |
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+ | 0.0146 | 500 | 0.0318 | - |
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+ | 0.0161 | 550 | 0.0037 | - |
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+ | 0.0175 | 600 | 0.0065 | - |
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+ | 0.0190 | 650 | 0.0401 | - |
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+ | 0.0204 | 700 | 0.0015 | - |
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+ | 0.0219 | 750 | 0.0043 | - |
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+ | 0.0233 | 800 | 0.0968 | - |
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+ | 0.0248 | 850 | 0.1695 | - |
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+ | 0.0263 | 900 | 0.0037 | - |
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+ | 0.0277 | 950 | 0.001 | - |
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+ | 0.0292 | 1000 | 0.0041 | - |
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+ | 0.0306 | 1050 | 0.0009 | - |
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+ | 0.0321 | 1100 | 0.0025 | - |
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+ | 0.0336 | 1150 | 0.0015 | - |
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+ | 0.0350 | 1200 | 0.0763 | - |
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+ | 0.0365 | 1250 | 0.2008 | - |
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+ | 0.0379 | 1300 | 0.0015 | - |
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+ | 0.0394 | 1350 | 0.0766 | - |
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+ | 0.0409 | 1400 | 0.2491 | - |
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+ | 0.0423 | 1450 | 0.1411 | - |
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+ | 0.0438 | 1500 | 0.0007 | - |
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+ | 0.0452 | 1550 | 0.0057 | - |
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+ | 0.0467 | 1600 | 0.0007 | - |
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+ | 0.0482 | 1650 | 0.1603 | - |
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+ | 0.0496 | 1700 | 0.0006 | - |
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+ | 0.0511 | 1750 | 0.0019 | - |
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+ | 0.0525 | 1800 | 0.0005 | - |
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+ | 0.0540 | 1850 | 0.0005 | - |
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+ | 0.0555 | 1900 | 0.2637 | - |
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+ | 0.0569 | 1950 | 0.0011 | - |
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+ | 0.0584 | 2000 | 0.0008 | - |
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+ | 0.0598 | 2050 | 0.0017 | - |
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+ | 0.0613 | 2100 | 0.0005 | - |
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+ | 0.0627 | 2150 | 0.0002 | - |
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+ | 0.0642 | 2200 | 0.0766 | - |
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+ | 0.0657 | 2250 | 0.0001 | - |
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+ | 0.0671 | 2300 | 0.0002 | - |
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+ | 0.0686 | 2350 | 0.0023 | - |
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+ | 0.0700 | 2400 | 0.0001 | - |
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+ | 0.0715 | 2450 | 0.0122 | - |
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+ | 0.0730 | 2500 | 0.001 | - |
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+ | 0.0744 | 2550 | 0.0006 | - |
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+ | 0.0759 | 2600 | 0.0056 | - |
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+ | 0.0773 | 2650 | 0.0022 | - |
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+ | 0.0788 | 2700 | 0.0002 | - |
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+ | 0.0803 | 2750 | 0.0213 | - |
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+ | 0.0817 | 2800 | 0.047 | - |
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+ | 0.0832 | 2850 | 0.0002 | - |
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+ | 0.0846 | 2900 | 0.0135 | - |
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+ | 0.0861 | 2950 | 0.0473 | - |
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+ | 0.0876 | 3000 | 0.0003 | - |
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+ | 0.0890 | 3050 | 0.0078 | - |
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+ | 0.0905 | 3100 | 0.0001 | - |
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+ | 0.0919 | 3150 | 0.0002 | - |
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+ | 0.0934 | 3200 | 0.008 | - |
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+ | 0.0949 | 3250 | 0.0005 | - |
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+ | 0.0963 | 3300 | 0.0002 | - |
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+ | 0.0978 | 3350 | 0.0062 | - |
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+ | 0.0992 | 3400 | 0.0002 | - |
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+ | 0.1007 | 3450 | 0.0002 | - |
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+ | 0.1021 | 3500 | 0.0007 | - |
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+ | 0.1036 | 3550 | 0.0017 | - |
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+ | 0.1051 | 3600 | 0.1652 | - |
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+ | 0.1065 | 3650 | 0.0011 | - |
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+ | 0.1080 | 3700 | 0.0 | - |
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+ | 0.1094 | 3750 | 0.0003 | - |
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+ | 0.1109 | 3800 | 0.0007 | - |
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+ | 0.1124 | 3850 | 0.0006 | - |
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+ | 0.1138 | 3900 | 0.0001 | - |
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+ | 0.1153 | 3950 | 0.002 | - |
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+ | 0.1167 | 4000 | 0.0001 | - |
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+ | 0.1182 | 4050 | 0.0004 | - |
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+ | 0.1197 | 4100 | 0.0003 | - |
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+ | 0.1211 | 4150 | 0.0295 | - |
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+ | 0.1226 | 4200 | 0.0012 | - |
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+ | 0.1240 | 4250 | 0.0004 | - |
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+ | 0.1255 | 4300 | 0.0003 | - |
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+ | 0.1270 | 4350 | 0.0364 | - |
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+ | 0.1284 | 4400 | 0.042 | - |
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+ | 0.1299 | 4450 | 0.0 | - |
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+ | 0.1313 | 4500 | 0.0 | - |
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+ | 0.1328 | 4550 | 0.0001 | - |
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+ | 0.1343 | 4600 | 0.0159 | - |
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+ | 0.1357 | 4650 | 0.0001 | - |
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+ | 0.1372 | 4700 | 0.0 | - |
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+ | 0.1386 | 4750 | 0.0004 | - |
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+ | 0.1401 | 4800 | 0.0409 | - |
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+ | 0.1415 | 4850 | 0.0411 | - |
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+ | 0.1430 | 4900 | 0.0001 | - |
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+ | 0.1445 | 4950 | 0.0002 | - |
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+ | 0.1459 | 5000 | 0.0 | - |
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+ | 0.1474 | 5050 | 0.1251 | - |
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+ | 0.1488 | 5100 | 0.0 | - |
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+ | 0.1503 | 5150 | 0.0001 | - |
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+ | 0.1518 | 5200 | 0.0 | - |
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+ | 0.1532 | 5250 | 0.0 | - |
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+ | 0.1547 | 5300 | 0.0466 | - |
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+ | 0.1561 | 5350 | 0.0 | - |
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+ | 0.1576 | 5400 | 0.0001 | - |
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+ | 0.1591 | 5450 | 0.0 | - |
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+ | 0.1605 | 5500 | 0.0254 | - |
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+ | 0.1620 | 5550 | 0.0001 | - |
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+ | 0.1634 | 5600 | 0.0002 | - |
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+ | 0.1649 | 5650 | 0.0 | - |
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+ | 0.1664 | 5700 | 0.0264 | - |
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+ | 0.1678 | 5750 | 0.0006 | - |
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+ | 0.1693 | 5800 | 0.0001 | - |
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+ | 0.1707 | 5850 | 0.0022 | - |
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+ | 0.1722 | 5900 | 0.0011 | - |
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+ | 0.1737 | 5950 | 0.1395 | - |
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+ | 0.1751 | 6000 | 0.0169 | - |
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+ | 0.1766 | 6050 | 0.0043 | - |
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+ | 0.1780 | 6100 | 0.1513 | - |
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+ | 0.1795 | 6150 | 0.0001 | - |
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+ | 0.1809 | 6200 | 0.0008 | - |
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+ | 0.1824 | 6250 | 0.0 | - |
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+ | 0.1839 | 6300 | 0.0009 | - |
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+ | 0.1853 | 6350 | 0.0002 | - |
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+ | 0.1868 | 6400 | 0.0001 | - |
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+ | 0.1882 | 6450 | 0.0002 | - |
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+ | 0.1897 | 6500 | 0.0534 | - |
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+ | 0.1912 | 6550 | 0.0002 | - |
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+ | 0.1926 | 6600 | 0.0001 | - |
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+ | 0.1941 | 6650 | 0.0007 | - |
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+ | 0.1955 | 6700 | 0.1641 | - |
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+ | 0.1970 | 6750 | 0.0001 | - |
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+ | 0.1985 | 6800 | 0.0012 | - |
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+ | 0.1999 | 6850 | 0.0035 | - |
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+ | 0.2014 | 6900 | 0.0006 | - |
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+ | 0.2028 | 6950 | 0.0001 | - |
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+ | 0.2043 | 7000 | 0.0107 | - |
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+ | 0.2058 | 7050 | 0.0001 | - |
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+ | 0.2072 | 7100 | 0.0028 | - |
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+ | 0.2087 | 7150 | 0.0004 | - |
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+ | 0.2101 | 7200 | 0.0 | - |
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+ | 0.2116 | 7250 | 0.0866 | - |
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+ | 0.2131 | 7300 | 0.0 | - |
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+ | 0.2145 | 7350 | 0.0001 | - |
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+ | 0.2160 | 7400 | 0.0 | - |
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+ | 0.2174 | 7450 | 0.0 | - |
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+ | 0.2189 | 7500 | 0.0001 | - |
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+ | 0.2203 | 7550 | 0.0 | - |
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+ | 0.2218 | 7600 | 0.0001 | - |
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+ | 0.2233 | 7650 | 0.0001 | - |
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+ | 0.2247 | 7700 | 0.0 | - |
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+ | 0.2262 | 7750 | 0.0532 | - |
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+ | 0.2276 | 7800 | 0.0 | - |
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+ | 0.2291 | 7850 | 0.0611 | - |
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+ | 0.2306 | 7900 | 0.0001 | - |
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+ | 0.2320 | 7950 | 0.0 | - |
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+ | 0.2335 | 8000 | 0.0001 | - |
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+ | 0.2349 | 8050 | 0.0 | - |
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+ | 0.2364 | 8100 | 0.0 | - |
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+ | 0.2379 | 8150 | 0.0 | - |
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+ | 0.2393 | 8200 | 0.0304 | - |
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+ | 0.2408 | 8250 | 0.0 | - |
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+ | 0.2422 | 8300 | 0.0253 | - |
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+ | 0.2437 | 8350 | 0.0 | - |
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+ | 0.2452 | 8400 | 0.0 | - |
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+ | 0.2466 | 8450 | 0.0 | - |
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+ | 0.2481 | 8500 | 0.0173 | - |
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+ | 0.2495 | 8550 | 0.0002 | - |
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+ | 0.2510 | 8600 | 0.0003 | - |
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+ | 0.2525 | 8650 | 0.0012 | - |
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+ | 0.2539 | 8700 | 0.1639 | - |
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+ | 0.2554 | 8750 | 0.0308 | - |
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+ | 0.2568 | 8800 | 0.0 | - |
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+ | 0.2583 | 8850 | 0.0 | - |
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+ | 0.2597 | 8900 | 0.068 | - |
349
+ | 0.2612 | 8950 | 0.0001 | - |
350
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351
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352
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353
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354
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355
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356
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357
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358
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359
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360
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361
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362
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363
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364
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365
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366
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367
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368
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369
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370
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371
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372
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373
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374
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375
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376
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377
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378
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379
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
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579
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580
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581
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582
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583
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584
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585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
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598
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599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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621
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622
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623
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624
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625
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626
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627
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628
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629
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630
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631
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632
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633
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634
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635
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636
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637
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638
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639
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640
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641
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
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659
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660
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661
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662
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663
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664
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665
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
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676
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677
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678
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679
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680
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681
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682
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683
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684
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685
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686
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687
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688
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689
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690
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691
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692
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693
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694
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695
+ | 0.7661 | 26250 | 0.0002 | - |
696
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697
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698
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699
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700
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701
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702
+ | 0.7763 | 26600 | 0.0002 | - |
703
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704
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705
+ | 0.7807 | 26750 | 0.0001 | - |
706
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707
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708
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709
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710
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711
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712
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713
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714
+ | 0.7938 | 27200 | 0.0001 | - |
715
+ | 0.7953 | 27250 | 0.0376 | - |
716
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717
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718
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719
+ | 0.8011 | 27450 | 0.0001 | - |
720
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721
+ | 0.8041 | 27550 | 0.0 | - |
722
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723
+ | 0.8070 | 27650 | 0.0 | - |
724
+ | 0.8084 | 27700 | 0.0 | - |
725
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726
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727
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728
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729
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730
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731
+ | 0.8186 | 28050 | 0.0 | - |
732
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733
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+
857
+ ### Framework Versions
858
+ - Python: 3.10.13
859
+ - SetFit: 1.0.3
860
+ - Sentence Transformers: 2.7.0
861
+ - spaCy: 3.7.4
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+ - Transformers: 4.36.2
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+ - PyTorch: 2.1.2
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+ - Datasets: 2.18.0
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+ - Tokenizers: 0.15.2
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+
867
+ ## Citation
868
+
869
+ ### BibTeX
870
+ ```bibtex
871
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
872
+ doi = {10.48550/ARXIV.2209.11055},
873
+ url = {https://arxiv.org/abs/2209.11055},
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+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
875
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
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+ publisher = {arXiv},
878
+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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