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Funnyworld1412
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ABSA_review_game_genshin-aspect

Text Classification
setfit
Safetensors
sentence-transformers
bert
absa
generated_from_setfit_trainer
Model card Files Community
Funnyworld1412 commited on
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e53f1bf
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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 +480 -0
  3. config.json +47 -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 +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: penambahan jumlah max resin:update qol loadout artefak, skip story, ringkasan
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+ story jika di skip, dan penambahan jumlah max resin mana min game udah 3 tahun
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+ gini gini aja gak ada perkembangan. apalagi hadiah untuk pemain selama 3 tahun
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+ tidak ada peningkatan
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+ - text: dialognya:adain fitur skip dialog gak penting , capek tangan mencetin layar
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+ doang , mana panjang , dialognya juga ga nyambung sama cerita aslinya ini
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+ - text: anak anak:istilah game kikir itu emang benar sih buat game ini, parah ngabisin
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+ waktu disuruh nguli trosss hadiah gak seberapa, event gede kecil sama aja reward
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+ dikit, bukannya gak bersyukur...tapi lu nya aja yg pelit. tidak ramah untuk player
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+ anak anak yang uang jajannya dikit, dikira anak anak pada kerja semua orang dewasa
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+ yang kerja aja gaji gak sampe buat topup segitu, minimal beri reward yang lumayan
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+ lah jangan kecil kecil mulu, dikira gacha itu murah... sekian terima kasih kikir
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+ impact
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+ - text: perubahan:jujur game nya bagus. grafik mantap. story lumayan. tapi developernya
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+ kikir ama buta tuli terhadap komunitasnya. tidak ada perubahan dalam segi quality
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+ of life dalam 3 tahun. ada beberapa qol yang di implementasi tapi kesanya tidak
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+ berguna. ada masalah dengan game dan kita kritik dev jadi tuli bisu bahkan buta.
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+ reward anniversary dan lantern rite juga sama selama 3 tahun. gak ada perubahan.
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+ percuma ngasih survey kepuasan tiap akhir patch kalau cman buat formalitas.
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+ - text: tulisan jaringan:tidak bisa login padahal jaringan bagus paket data juga masih
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+ banyak, dan dilayar ada tulisan jaringan error, selama saya masih gabisa login
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+ dan main saya bakal tetap kasih bintang 1
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+ pipeline_tag: text-classification
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+ inference: false
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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:** [Funnyworld1412/ABSA_review_game_genshin-aspect](https://huggingface.co/Funnyworld1412/ABSA_review_game_genshin-aspect)
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+ - **SetFitABSA Polarity Model:** [Funnyworld1412/ABSA_review_game_genshin-polarity](https://huggingface.co/Funnyworld1412/ABSA_review_game_genshin-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>'story:saranku developer harus menciptakan sebuah story yang sangat menarik, agar tidak kehilangan para player karena masalahnya banyak player yg tidak bertahan lama karena repetitif dan monoton tiap update, size makin gede doang yg isinya cuma chest baru itupun sampah, puzzle yg makin lama makin rumit tapi chest nya sampah, story kebanyakan npc teyvat story utama punya mc dilupain gak difokusin , map kalo udah kosong ya nyampah bikin size gede doang. main 3 tahun rasanya monoton, perkembangan buruk'</li><li>'reward:tolong ditambah lagi reward untuk gachanya, untuk player lama kesulitan mendapatkan primo karena sudah tidak ada lagi quest dan eksplorasi juga sudah 100 . dasar developer kapitalis, game ini makin lama makin monoton dan tidak ramah untuk player lama yang kekurangan bahan untuk gacha karakter'</li><li>'event:cuman saran jangan terlalu pelit.. biar para player gak kabur sama game sebelah hadiah event quest di perbaiki.... udah nunggu event lama lama hadiah cuman gitu gitu aja... sampek event selesai primogemnya buat 10 pull gacha gak cukup.... tingakat kesulitan beda hadiah sama saja... lama lama yang main pada kabur kalok terlalu pelit.. dan 1 lagi jariang mohon di perbaiki untuk server indonya trimaksih'</li></ul> |
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+ | no aspect | <ul><li>'saranku developer:saranku developer harus menciptakan sebuah story yang sangat menarik, agar tidak kehilangan para player karena masalahnya banyak player yg tidak bertahan lama karena repetitif dan monoton tiap update, size makin gede doang yg isinya cuma chest baru itupun sampah, puzzle yg makin lama makin rumit tapi chest nya sampah, story kebanyakan npc teyvat story utama punya mc dilupain gak difokusin , map kalo udah kosong ya nyampah bikin size gede doang. main 3 tahun rasanya monoton, perkembangan buruk'</li><li>'story:saranku developer harus menciptakan sebuah story yang sangat menarik, agar tidak kehilangan para player karena masalahnya banyak player yg tidak bertahan lama karena repetitif dan monoton tiap update, size makin gede doang yg isinya cuma chest baru itupun sampah, puzzle yg makin lama makin rumit tapi chest nya sampah, story kebanyakan npc teyvat story utama punya mc dilupain gak difokusin , map kalo udah kosong ya nyampah bikin size gede doang. main 3 tahun rasanya monoton, perkembangan buruk'</li><li>'player:saranku developer harus menciptakan sebuah story yang sangat menarik, agar tidak kehilangan para player karena masalahnya banyak player yg tidak bertahan lama karena repetitif dan monoton tiap update, size makin gede doang yg isinya cuma chest baru itupun sampah, puzzle yg makin lama makin rumit tapi chest nya sampah, story kebanyakan npc teyvat story utama punya mc dilupain gak difokusin , map kalo udah kosong ya nyampah bikin size gede doang. main 3 tahun rasanya monoton, perkembangan buruk'</li></ul> |
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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_review_game_genshin-aspect",
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+ "Funnyworld1412/ABSA_review_game_genshin-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 | 4 | 49.9079 | 94 |
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+
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+ | Label | Training Sample Count |
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+ |:----------|:----------------------|
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+ | no aspect | 2281 |
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+ | aspect | 477 |
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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: 10
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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 | 0.25 | - |
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+ | 0.0036 | 50 | 0.331 | - |
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+ | 0.0073 | 100 | 0.5002 | - |
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+ | 0.0109 | 150 | 0.2904 | - |
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+ | 0.0145 | 200 | 0.3791 | - |
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+ | 0.0181 | 250 | 0.2253 | - |
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+ | 0.0218 | 300 | 0.1909 | - |
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+ | 0.0254 | 350 | 0.2504 | - |
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+ | 0.0290 | 400 | 0.1241 | - |
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+ | 0.0326 | 450 | 0.1021 | - |
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+ | 0.0363 | 500 | 0.0985 | - |
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+ | 0.0399 | 550 | 0.3831 | - |
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+ | 0.0435 | 600 | 0.1841 | - |
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+ | 0.0471 | 650 | 0.2487 | - |
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+ | 0.0508 | 700 | 0.1573 | - |
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+ | 0.0544 | 750 | 0.0499 | - |
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+ | 0.0580 | 800 | 0.2214 | - |
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+ | 0.0616 | 850 | 0.1427 | - |
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+ | 0.0653 | 900 | 0.3544 | - |
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+ | 0.0689 | 950 | 0.042 | - |
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+ | 0.0725 | 1000 | 0.2918 | - |
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+ | 0.0761 | 1050 | 0.0134 | - |
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+ | 0.0798 | 1100 | 0.1933 | - |
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+ | 0.0834 | 1150 | 0.0115 | - |
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+ | 0.0870 | 1200 | 0.2393 | - |
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+ | 0.0906 | 1250 | 0.2625 | - |
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+ | 0.0943 | 1300 | 0.1496 | - |
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+ | 0.0979 | 1350 | 0.1417 | - |
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+ | 0.1015 | 1400 | 0.2111 | - |
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+ | 0.1051 | 1450 | 0.2158 | - |
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+ | 0.1088 | 1500 | 0.1378 | - |
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+ | 0.1124 | 1550 | 0.0988 | - |
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+ | 0.1160 | 1600 | 0.1183 | - |
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+ | 0.1197 | 1650 | 0.324 | - |
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+ | 0.1233 | 1700 | 0.3722 | - |
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+ | 0.1269 | 1750 | 0.1696 | - |
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+ | 0.1305 | 1800 | 0.2893 | - |
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+ | 0.1342 | 1850 | 0.198 | - |
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+ | 0.1378 | 1900 | 0.2854 | - |
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+ | 0.1414 | 1950 | 0.3339 | - |
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+ | 0.1450 | 2000 | 0.0783 | - |
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+ | 0.1487 | 2050 | 0.014 | - |
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+ | 0.1523 | 2100 | 0.0205 | - |
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+ | 0.1559 | 2150 | 0.0151 | - |
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+ | 0.1595 | 2200 | 0.3783 | - |
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+ | 0.1632 | 2250 | 0.381 | - |
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+ | 0.1668 | 2300 | 0.144 | - |
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+ | 0.1704 | 2350 | 0.0023 | - |
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+ | 0.1740 | 2400 | 0.1903 | - |
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+ | 0.1777 | 2450 | 0.0033 | - |
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+ | 0.1813 | 2500 | 0.0039 | - |
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+ | 0.1849 | 2550 | 0.0019 | - |
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+ | 0.1885 | 2600 | 0.0565 | - |
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+ | 0.1922 | 2650 | 0.1551 | - |
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+ | 0.1958 | 2700 | 0.0729 | - |
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+ | 0.1994 | 2750 | 0.0272 | - |
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+ | 0.2030 | 2800 | 0.495 | - |
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+ | 0.2067 | 2850 | 0.0396 | - |
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+ | 0.2103 | 2900 | 0.2288 | - |
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+ | 0.2139 | 2950 | 0.0077 | - |
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+ | 0.2175 | 3000 | 0.0642 | - |
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+ | 0.2212 | 3050 | 0.0037 | - |
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+ | 0.2248 | 3100 | 0.2447 | - |
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+ | 0.2284 | 3150 | 0.0097 | - |
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+ | 0.2321 | 3200 | 0.0011 | - |
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+ | 0.2357 | 3250 | 0.1254 | - |
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+ | 0.2393 | 3300 | 0.0046 | - |
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+ | 0.2429 | 3350 | 0.0127 | - |
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+ | 0.2466 | 3400 | 0.0093 | - |
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+ | 0.2502 | 3450 | 0.0005 | - |
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+ | 0.2538 | 3500 | 0.0022 | - |
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+ | 0.2574 | 3550 | 0.0005 | - |
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+ | 0.2611 | 3600 | 0.0002 | - |
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+ | 0.2647 | 3650 | 0.0231 | - |
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+ | 0.2683 | 3700 | 0.0016 | - |
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+ | 0.2719 | 3750 | 0.1945 | - |
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+ | 0.2756 | 3800 | 0.002 | - |
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+ | 0.2792 | 3850 | 0.0235 | - |
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+ | 0.2828 | 3900 | 0.006 | - |
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+ | 0.2864 | 3950 | 0.0003 | - |
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+ | 0.2901 | 4000 | 0.007 | - |
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+ | 0.2937 | 4050 | 0.0227 | - |
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+ | 0.2973 | 4100 | 0.1794 | - |
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+ | 0.3009 | 4150 | 0.2629 | - |
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+ | 0.3046 | 4200 | 0.3005 | - |
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+ | 0.3082 | 4250 | 0.1974 | - |
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+ | 0.3118 | 4300 | 0.001 | - |
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+ | 0.3154 | 4350 | 0.0123 | - |
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+ | 0.3191 | 4400 | 0.0027 | - |
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+ | 0.3227 | 4450 | 0.0002 | - |
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+ | 0.3263 | 4500 | 0.0005 | - |
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+ | 0.3299 | 4550 | 0.0002 | - |
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+ | 0.3336 | 4600 | 0.0007 | - |
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+ | 0.3372 | 4650 | 0.0332 | - |
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+ | 0.3408 | 4700 | 0.052 | - |
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+ | 0.3445 | 4750 | 0.0103 | - |
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+ | 0.3481 | 4800 | 0.0067 | - |
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+ | 0.3517 | 4850 | 0.0003 | - |
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+ | 0.3553 | 4900 | 0.0008 | - |
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+ | 0.3590 | 4950 | 0.0088 | - |
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+ | 0.3626 | 5000 | 0.0002 | - |
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+ | 0.3662 | 5050 | 0.0111 | - |
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+ | 0.3698 | 5100 | 0.0836 | - |
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+ | 0.3735 | 5150 | 0.0001 | - |
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+ | 0.3771 | 5200 | 0.2398 | - |
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+ | 0.3807 | 5250 | 0.0002 | - |
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+ | 0.3843 | 5300 | 0.1435 | - |
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+ | 0.3880 | 5350 | 0.0001 | - |
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+ | 0.3916 | 5400 | 0.0296 | - |
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+ | 0.3952 | 5450 | 0.0003 | - |
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+ | 0.3988 | 5500 | 0.1126 | - |
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+ | 0.4025 | 5550 | 0.0009 | - |
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+ | 0.4061 | 5600 | 0.0055 | - |
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+ | 0.4097 | 5650 | 0.0031 | - |
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+ | 0.4133 | 5700 | 0.1929 | - |
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+ | 0.4170 | 5750 | 0.0002 | - |
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+ | 0.4206 | 5800 | 0.2565 | - |
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+ | 0.4242 | 5850 | 0.0002 | - |
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+ | 0.4278 | 5900 | 0.0033 | - |
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+ | 0.4315 | 5950 | 0.0011 | - |
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+ | 0.4351 | 6000 | 0.0001 | - |
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+ | 0.4387 | 6050 | 0.0004 | - |
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+ | 0.4423 | 6100 | 0.0003 | - |
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+ | 0.4460 | 6150 | 0.1076 | - |
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+ | 0.4496 | 6200 | 0.0011 | - |
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+ | 0.4532 | 6250 | 0.0034 | - |
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+ | 0.4569 | 6300 | 0.0176 | - |
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+ | 0.4605 | 6350 | 0.2883 | - |
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+ | 0.4641 | 6400 | 0.0 | - |
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+ | 0.4677 | 6450 | 0.0172 | - |
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+ | 0.4714 | 6500 | 0.0014 | - |
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+ | 0.4750 | 6550 | 0.0571 | - |
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+ | 0.4786 | 6600 | 0.0287 | - |
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+ | 0.4822 | 6650 | 0.1461 | - |
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+ | 0.4859 | 6700 | 0.2333 | - |
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+ | 0.4895 | 6750 | 0.1468 | - |
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+ | 0.4931 | 6800 | 0.0005 | - |
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+ | 0.4967 | 6850 | 0.0039 | - |
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+ | 0.5004 | 6900 | 0.0004 | - |
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+ | 0.5040 | 6950 | 0.0008 | - |
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+ | 0.5076 | 7000 | 0.0004 | - |
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+ | 0.5112 | 7050 | 0.0005 | - |
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+ | 0.5149 | 7100 | 0.001 | - |
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+ | 0.5185 | 7150 | 0.0041 | - |
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+ | 0.5221 | 7200 | 0.0157 | - |
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+ | 0.5257 | 7250 | 0.0228 | - |
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+ | 0.5294 | 7300 | 0.0002 | - |
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+ | 0.5330 | 7350 | 0.0004 | - |
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+ | 0.5366 | 7400 | 0.0081 | - |
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+ | 0.5402 | 7450 | 0.0004 | - |
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+ | 0.5439 | 7500 | 0.1227 | - |
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+ | 0.5475 | 7550 | 0.0001 | - |
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+ | 0.5511 | 7600 | 0.0006 | - |
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+ | 0.5547 | 7650 | 0.0003 | - |
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+ | 0.5584 | 7700 | 0.0475 | - |
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+ | 0.5620 | 7750 | 0.1848 | - |
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+ | 0.5656 | 7800 | 0.0007 | - |
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+ | 0.5693 | 7850 | 0.001 | - |
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+ | 0.5729 | 7900 | 0.0002 | - |
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+ | 0.5765 | 7950 | 0.0018 | - |
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+ | 0.5801 | 8000 | 0.0009 | - |
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+ | 0.5838 | 8050 | 0.0019 | - |
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+ | 0.5874 | 8100 | 0.0001 | - |
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+ | 0.5910 | 8150 | 0.0012 | - |
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+
438
+ ### Framework Versions
439
+ - Python: 3.10.13
440
+ - SetFit: 1.0.3
441
+ - Sentence Transformers: 3.0.1
442
+ - spaCy: 3.7.5
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+ - Transformers: 4.36.2
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+ - PyTorch: 2.1.2
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+ - Datasets: 2.19.2
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+ - Tokenizers: 0.15.2
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+
448
+ ## Citation
449
+
450
+ ### BibTeX
451
+ ```bibtex
452
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
453
+ doi = {10.48550/ARXIV.2209.11055},
454
+ url = {https://arxiv.org/abs/2209.11055},
455
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
456
+ 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},
458
+ publisher = {arXiv},
459
+ 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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