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Funnyworld1412
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ABSA_mpnet_MiniLM-L6-aspect

Text Classification
setfit
Safetensors
sentence-transformers
bert
absa
generated_from_setfit_trainer
Eval Results
Model card Files
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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 +473 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +9 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +20 -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": 384,
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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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+ base_model: sentence-transformers/all-MiniLM-L6-v2
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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: hp:game yg grafiknya standar boros batrai bikin hp cepat panas game satunya
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+ brawlstar ga
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+ - text: game:game cocok indonesia gw main game dibilang berat squad buster jaringan
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+ game berat bagus squad buster main koneksi terputus koneksi aman aman aja mohon
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+ perbaiki jaringan
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+ - text: sinyal:prmainannya bagus sinyal diperbaiki maen game online gak bagus2 aja
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+ pingnya eh maen squad busters jaringannya hilang2 pas match klok sinyal udah hilang
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+ masuk tulisan server konek muat ulang gak masuk in game saran tolong diperbaiki
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+ ya min klok grafik gameplay udah bagus
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+ - text: saran semoga game:gamenya bagus kendala game nya kadang kadang suka jaringan
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+ jaringan bagus saran semoga game nya ditingkatkan disaat update
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+ - text: gameplay:gameplay nya bagus gk match nya optimal main kadang suka lag gitu
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+ sinyal nya bagus tolong supercell perbaiki sinyal
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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 with sentence-transformers/all-MiniLM-L6-v2
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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: 0.8316929133858267
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+ name: Accuracy
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+ ---
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+
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+ # SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2
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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). This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 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 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 body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
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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:** [Funnyworld1412/ABSA_mpnet_MiniLM-L6-aspect](https://huggingface.co/Funnyworld1412/ABSA_mpnet_MiniLM-L6-aspect)
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+ - **SetFitABSA Polarity Model:** [Funnyworld1412/ABSA_mpnet_MiniLM-L6-polarity](https://huggingface.co/Funnyworld1412/ABSA_mpnet_MiniLM-L6-polarity)
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+ - **Maximum Sequence Length:** 256 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>'pencarian lawan:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'game:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'bugnya:bugnya nakal banget y coc cr aja sukanya ngebug pas match suka hitam match relog kalo udah relog lawan udah 1 2 mahkota kecewa sih bintang nya 1 aja bug nya diurus bintang lawannya kadang g setara levelnya dahlah gk suka banget kalo main 2 vs 2 temen suka banget afk coba fitur report'</li></ul> |
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+ | no aspect | <ul><li>'player trophy mahkotanya jaraknya:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'peleton akun perbedaan level:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'y coc cr:bugnya nakal banget y coc cr aja sukanya ngebug pas match suka hitam match relog kalo udah relog lawan udah 1 2 mahkota kecewa sih bintang nya 1 aja bug nya diurus bintang lawannya kadang g setara levelnya dahlah gk suka banget kalo main 2 vs 2 temen suka banget afk coba fitur report'</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** | 0.8317 |
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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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+ "Funnyworld1412/ABSA_mpnet_MiniLM-L6-aspect",
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+ "Funnyworld1412/ABSA_mpnet_MiniLM-L6-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 | 29.9357 | 80 |
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+
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+ | Label | Training Sample Count |
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+ |:----------|:----------------------|
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+ | no aspect | 3834 |
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+ | aspect | 1266 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (4, 4)
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+ - num_epochs: (1, 1)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 5
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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: False
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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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+ | 0.0001 | 1 | 0.2801 | - |
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+ | 0.0039 | 50 | 0.2365 | - |
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+ | 0.0078 | 100 | 0.1068 | - |
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+ | 0.0118 | 150 | 0.3401 | - |
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+ | 0.0157 | 200 | 0.2112 | - |
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+ | 0.0196 | 250 | 0.3529 | - |
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+ | 0.0235 | 300 | 0.2338 | - |
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+ | 0.0275 | 350 | 0.2039 | - |
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+ | 0.0314 | 400 | 0.2006 | - |
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+ | 0.0353 | 450 | 0.2939 | - |
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+ | 0.0392 | 500 | 0.2053 | - |
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+ | 0.0431 | 550 | 0.2036 | - |
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+ | 0.0471 | 600 | 0.2229 | - |
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+ | 0.0510 | 650 | 0.105 | - |
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+ | 0.0549 | 700 | 0.2222 | - |
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+ | 0.0588 | 750 | 0.1815 | - |
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+ | 0.0627 | 800 | 0.2915 | - |
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+ | 0.0667 | 850 | 0.276 | - |
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+ | 0.0706 | 900 | 0.1682 | - |
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+ | 0.0745 | 950 | 0.2328 | - |
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+ | 0.0784 | 1000 | 0.2422 | - |
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+ | 0.0824 | 1050 | 0.2753 | - |
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+ | 0.0863 | 1100 | 0.2292 | - |
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+ | 0.0902 | 1150 | 0.0791 | - |
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+ | 0.0941 | 1200 | 0.3849 | - |
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+ | 0.0980 | 1250 | 0.0964 | - |
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+ | 0.1020 | 1300 | 0.1612 | - |
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+ | 0.1059 | 1350 | 0.2755 | - |
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+ | 0.1098 | 1400 | 0.1133 | - |
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+ | 0.1137 | 1450 | 0.038 | - |
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+ | 0.1176 | 1500 | 0.3195 | - |
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+ | 0.1216 | 1550 | 0.0091 | - |
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+ | 0.1255 | 1600 | 0.3148 | - |
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+ | 0.1294 | 1650 | 0.1693 | - |
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+ | 0.1333 | 1700 | 0.2411 | - |
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+ | 0.1373 | 1750 | 0.2463 | - |
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+ | 0.1412 | 1800 | 0.2807 | - |
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+ | 0.1451 | 1850 | 0.112 | - |
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+ | 0.1490 | 1900 | 0.2623 | - |
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+ | 0.1529 | 1950 | 0.2465 | - |
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+ | 0.1569 | 2000 | 0.4591 | - |
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+ | 0.1608 | 2050 | 0.0556 | - |
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+ | 0.1647 | 2100 | 0.0962 | - |
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+ | 0.1686 | 2150 | 0.4525 | - |
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+ | 0.1725 | 2200 | 0.2674 | - |
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+ | 0.1765 | 2250 | 0.1513 | - |
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+ | 0.1804 | 2300 | 0.3457 | - |
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+ | 0.1843 | 2350 | 0.1415 | - |
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+ | 0.1882 | 2400 | 0.0454 | - |
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+ | 0.1922 | 2450 | 0.0156 | - |
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+ | 0.1961 | 2500 | 0.2741 | - |
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+ | 0.2 | 2550 | 0.1334 | - |
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+ | 0.2039 | 2600 | 0.1838 | - |
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+ | 0.2078 | 2650 | 0.1346 | - |
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+ | 0.2118 | 2700 | 0.1022 | - |
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+ | 0.2157 | 2750 | 0.3999 | - |
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+ | 0.2196 | 2800 | 0.0953 | - |
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+ | 0.2235 | 2850 | 0.1201 | - |
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+ | 0.2275 | 2900 | 0.111 | - |
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+ | 0.2314 | 2950 | 0.1081 | - |
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+ | 0.2353 | 3000 | 0.1926 | - |
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+ | 0.2392 | 3050 | 0.1047 | - |
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+ | 0.2431 | 3100 | 0.2367 | - |
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+ | 0.2471 | 3150 | 0.2034 | - |
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+ | 0.2510 | 3200 | 0.0824 | - |
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+ | 0.2549 | 3250 | 0.0338 | - |
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+ | 0.2588 | 3300 | 0.2468 | - |
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+ | 0.2627 | 3350 | 0.0082 | - |
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+ | 0.2667 | 3400 | 0.0023 | - |
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+ | 0.2706 | 3450 | 0.1106 | - |
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+ | 0.2745 | 3500 | 0.1315 | - |
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+ | 0.2784 | 3550 | 0.004 | - |
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+ | 0.2824 | 3600 | 0.0836 | - |
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+ | 0.2863 | 3650 | 0.2716 | - |
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+ | 0.2902 | 3700 | 0.1873 | - |
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+ | 0.2941 | 3750 | 0.4066 | - |
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+ | 0.2980 | 3800 | 0.1448 | - |
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+ | 0.3020 | 3850 | 0.0137 | - |
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+ | 0.3059 | 3900 | 0.3471 | - |
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+ | 0.3098 | 3950 | 0.1144 | - |
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+ | 0.3137 | 4000 | 0.0596 | - |
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+ | 0.3176 | 4050 | 0.0377 | - |
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+ | 0.3216 | 4100 | 0.3316 | - |
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+ | 0.3255 | 4150 | 0.0709 | - |
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+ | 0.3294 | 4200 | 0.0515 | - |
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+ | 0.3333 | 4250 | 0.2029 | - |
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+ | 0.3373 | 4300 | 0.1191 | - |
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+ | 0.3412 | 4350 | 0.2397 | - |
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+ | 0.3451 | 4400 | 0.492 | - |
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+ | 0.3490 | 4450 | 0.1178 | - |
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+ | 0.3529 | 4500 | 0.3647 | - |
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+ | 0.3569 | 4550 | 0.0098 | - |
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+ | 0.3608 | 4600 | 0.2114 | - |
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+ | 0.3647 | 4650 | 0.2392 | - |
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+ | 0.3686 | 4700 | 0.2194 | - |
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+ | 0.3725 | 4750 | 0.0578 | - |
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+ | 0.3765 | 4800 | 0.0771 | - |
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+ | 0.3804 | 4850 | 0.1582 | - |
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+ | 0.3843 | 4900 | 0.0643 | - |
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+ | 0.3882 | 4950 | 0.1372 | - |
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+ | 0.3922 | 5000 | 0.0308 | - |
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+ | 0.3961 | 5050 | 0.1247 | - |
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+ | 0.4 | 5100 | 0.3076 | - |
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+ | 0.4039 | 5150 | 0.1152 | - |
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+ | 0.4078 | 5200 | 0.2112 | - |
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+ | 0.4118 | 5250 | 0.0042 | - |
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+ | 0.4157 | 5300 | 0.0869 | - |
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+ | 0.4196 | 5350 | 0.0196 | - |
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+ | 0.4235 | 5400 | 0.2406 | - |
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+ | 0.4275 | 5450 | 0.3306 | - |
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+ | 0.4314 | 5500 | 0.2328 | - |
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+ | 0.4353 | 5550 | 0.008 | - |
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+ | 0.4392 | 5600 | 0.0388 | - |
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+ | 0.4431 | 5650 | 0.3812 | - |
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+ | 0.4471 | 5700 | 0.6268 | - |
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+ | 0.4510 | 5750 | 0.4426 | - |
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+ | 0.4549 | 5800 | 0.1407 | - |
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+ | 0.4588 | 5850 | 0.297 | - |
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+ | 0.4627 | 5900 | 0.2657 | - |
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+ | 0.4667 | 5950 | 0.1767 | - |
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+ | 0.4706 | 6000 | 0.0152 | - |
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+ | 0.4745 | 6050 | 0.2344 | - |
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+ | 0.4784 | 6100 | 0.0447 | - |
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+ | 0.4824 | 6150 | 0.0675 | - |
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+ | 0.4863 | 6200 | 0.3086 | - |
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+ | 0.4902 | 6250 | 0.5258 | - |
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+ | 0.4941 | 6300 | 0.0826 | - |
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+ | 0.4980 | 6350 | 0.0079 | - |
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+ | 0.5020 | 6400 | 0.1817 | - |
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+ | 0.5059 | 6450 | 0.0767 | - |
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+ | 0.5098 | 6500 | 0.0221 | - |
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+ | 0.5137 | 6550 | 0.0419 | - |
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+ | 0.5176 | 6600 | 0.2452 | - |
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+ | 0.5216 | 6650 | 0.0232 | - |
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+ | 0.5255 | 6700 | 0.0804 | - |
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+ | 0.5294 | 6750 | 0.1752 | - |
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+ | 0.5333 | 6800 | 0.0127 | - |
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+ | 0.5373 | 6850 | 0.0454 | - |
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+ | 0.5412 | 6900 | 0.1759 | - |
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+ | 0.5451 | 6950 | 0.0435 | - |
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+ | 0.5490 | 7000 | 0.0109 | - |
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+ | 0.5529 | 7050 | 0.0162 | - |
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+ | 0.5569 | 7100 | 0.0133 | - |
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+ | 0.5608 | 7150 | 0.2363 | - |
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+ | 0.5647 | 7200 | 0.4987 | - |
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+ | 0.5686 | 7250 | 0.1149 | - |
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+ | 0.5725 | 7300 | 0.4613 | - |
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+ | 0.5765 | 7350 | 0.3837 | - |
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+ | 0.5804 | 7400 | 0.2439 | - |
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+ | 0.5843 | 7450 | 0.0014 | - |
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+ | 0.5882 | 7500 | 0.0177 | - |
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+ | 0.5922 | 7550 | 0.0051 | - |
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+ | 0.5961 | 7600 | 0.0418 | - |
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+ | 0.6 | 7650 | 0.0061 | - |
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+ | 0.6039 | 7700 | 0.2205 | - |
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+ | 0.6078 | 7750 | 0.1769 | - |
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+ | 0.6118 | 7800 | 0.0071 | - |
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+ | 0.6157 | 7850 | 0.2271 | - |
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+ | 0.6196 | 7900 | 0.3049 | - |
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+ | 0.6235 | 7950 | 0.0016 | - |
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+ | 0.6275 | 8000 | 0.2263 | - |
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+ | 0.6314 | 8050 | 0.0057 | - |
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+ | 0.6353 | 8100 | 0.1408 | - |
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+ | 0.6392 | 8150 | 0.0303 | - |
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+ | 0.6431 | 8200 | 0.0026 | - |
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+ | 0.6471 | 8250 | 0.1743 | - |
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+ | 0.6510 | 8300 | 0.2078 | - |
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+ | 0.6549 | 8350 | 0.1764 | - |
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+ | 0.6588 | 8400 | 0.0127 | - |
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+ | 0.6627 | 8450 | 0.2435 | - |
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+ | 0.6667 | 8500 | 0.0527 | - |
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+ | 0.6706 | 8550 | 0.247 | - |
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+ | 0.6745 | 8600 | 0.002 | - |
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+ | 0.6784 | 8650 | 0.0087 | - |
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+ | 0.6824 | 8700 | 0.1866 | - |
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+ | 0.6863 | 8750 | 0.0087 | - |
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+ | 0.6902 | 8800 | 0.1589 | - |
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+ | 0.6941 | 8850 | 0.1848 | - |
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+ | 0.6980 | 8900 | 0.0298 | - |
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+ | 0.7020 | 8950 | 0.0081 | - |
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+ | 0.7059 | 9000 | 0.3057 | - |
355
+ | 0.7098 | 9050 | 0.2059 | - |
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360
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361
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362
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364
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365
+ | 0.7490 | 9550 | 0.013 | - |
366
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367
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368
+ | 0.7608 | 9700 | 0.0054 | - |
369
+ | 0.7647 | 9750 | 0.1869 | - |
370
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371
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372
+ | 0.7765 | 9900 | 0.0057 | - |
373
+ | 0.7804 | 9950 | 0.0056 | - |
374
+ | 0.7843 | 10000 | 0.0043 | - |
375
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376
+ | 0.7922 | 10100 | 0.6808 | - |
377
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378
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379
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380
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381
+ | 0.8118 | 10350 | 0.0653 | - |
382
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383
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384
+ | 0.8235 | 10500 | 0.1561 | - |
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
+ | 0.8549 | 10900 | 0.2045 | - |
393
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394
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395
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396
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397
+ | 0.8745 | 11150 | 0.0047 | - |
398
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399
+ | 0.8824 | 11250 | 0.2204 | - |
400
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401
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403
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404
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405
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416
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417
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420
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422
+ | 0.9725 | 12400 | 0.0313 | - |
423
+ | 0.9765 | 12450 | 0.0925 | - |
424
+ | 0.9804 | 12500 | 0.1742 | - |
425
+ | 0.9843 | 12550 | 0.2294 | - |
426
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427
+ | 0.9922 | 12650 | 0.038 | - |
428
+ | 0.9961 | 12700 | 0.1866 | - |
429
+ | 1.0 | 12750 | 0.0141 | 0.2274 |
430
+
431
+ ### Framework Versions
432
+ - Python: 3.10.13
433
+ - SetFit: 1.0.3
434
+ - Sentence Transformers: 3.0.1
435
+ - spaCy: 3.7.5
436
+ - Transformers: 4.36.2
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+ - PyTorch: 2.1.2
438
+ - Datasets: 2.19.2
439
+ - Tokenizers: 0.15.2
440
+
441
+ ## Citation
442
+
443
+ ### BibTeX
444
+ ```bibtex
445
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
446
+ doi = {10.48550/ARXIV.2209.11055},
447
+ url = {https://arxiv.org/abs/2209.11055},
448
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
449
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
450
+ title = {Efficient Few-Shot Learning Without Prompts},
451
+ publisher = {arXiv},
452
+ year = {2022},
453
+ copyright = {Creative Commons Attribution 4.0 International}
454
+ }
455
+ ```
456
+
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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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+
469
+ <!--
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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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