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pahri
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setfit-indo-resto-RM-ibu-imas-aspect

Text Classification
setfit
Safetensors
sentence-transformers
bert
absa
generated_from_setfit_trainer
Eval Results
Model card Files Community
pahri commited on
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1 Parent(s): 35aec36

Add SetFit ABSA model

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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +685 -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: Suasana:Tempatnya ramai sekali dan ngantei banget. Suasana di dalam resto
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+ sangat panas dan padat. Makanannya enak enak.
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+ - text: bener2 pedes puolll:Rasanya sgt gak cocok dilidah gue orang bekasi.. ayamnya
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+ ayam kampung sih tp kecil bgt (beli yg dada).. terus tempe bacem sgt padet dan
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+ tahunya enak sih.. untuk sambel pedes bgt bener2 pedes puolll, tp rasanya gasukaa.
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+ - text: gang:Suasana di dalam resto sangat panas dan padat. Makanannya enak enak.
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+ Dan restonya ada di beberapa tempat dalam satu gang.
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+ - text: tempe:Menu makanannya khas Sunda ada ayam, pepes ikan, babat, tahu, tempe,
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+ sayur-sayur. Tidak banyak variasinya tapi kualitas rasanya oke. Saat itu pesen
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+ ayam bakar, jukut goreng, tempe sama pepes tahu. Ini semuanya enak (menurut pendapat
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+ pribadi).
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+ - text: 'babat:Kemaren kebetulan makan babat sama nyobain cumi, buat tekstur babatnya
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+ itu engga alot sama sekali dan tidak amis, sedangkan buat cumi utuh lumayan gede
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+ juga tekstur kenyel kenyelnya dapet dan mateng juga sampe ke dalem. '
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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: 0.80625
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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:** [pahri/setfit-indo-resto-RM-ibu-imas-aspect](https://huggingface.co/pahri/setfit-indo-resto-RM-ibu-imas-aspect)
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+ - **SetFitABSA Polarity Model:** [pahri/setfit-indo-resto-RM-ibu-imas-polarity](https://huggingface.co/pahri/setfit-indo-resto-RM-ibu-imas-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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+ | no aspect | <ul><li>'ambel leuncanya:ambel leuncanya enak terus pedesss'</li><li>'Warung Sunda:Warung Sunda murah meriah dan makanannya enak. Favorit selada air krispi dan ayam bakar'</li><li>'makanannya:Warung Sunda murah meriah dan makanannya enak. Favorit selada air krispi dan ayam bakar'</li></ul> |
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+ | aspect | <ul><li>'ayam bakar:Warung Sunda murah meriah dan makanannya enak. Favorit selada air krispi dan ayam bakar'</li><li>'Ayam bakar:Ayam bakar,sambel leunca sambel terasi merah enak banget 9/10, perkedel jagung 8/10 makan pakai sambel mantap. Makan berdua sekitar 77k'</li><li>'sambel terasi merah:Ayam bakar,sambel leunca sambel terasi merah enak banget 9/10, perkedel jagung 8/10 makan pakai sambel mantap. Makan berdua sekitar 77k'</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** | 0.8063 |
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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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+ "pahri/setfit-indo-resto-RM-ibu-imas-aspect",
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+ "pahri/setfit-indo-resto-RM-ibu-imas-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 | 37.7180 | 93 |
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+
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+ | Label | Training Sample Count |
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+ |:----------|:----------------------|
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+ | no aspect | 371 |
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+ | aspect | 51 |
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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 | 0.4225 | - |
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+ | 0.0021 | 50 | 0.2528 | - |
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+ | 0.0043 | 100 | 0.3611 | - |
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+ | 0.0064 | 150 | 0.2989 | - |
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+ | 0.0085 | 200 | 0.2907 | - |
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+ | 0.0107 | 250 | 0.1609 | - |
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+ | 0.0128 | 300 | 0.3534 | - |
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+ | 0.0149 | 350 | 0.1294 | - |
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+ | 0.0171 | 400 | 0.2797 | - |
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+ | 0.0192 | 450 | 0.3119 | - |
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+ | 0.0213 | 500 | 0.004 | - |
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+ | 0.0235 | 550 | 0.1057 | - |
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+ | 0.0256 | 600 | 0.1049 | - |
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+ | 0.0277 | 650 | 0.1601 | - |
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+ | 0.0299 | 700 | 0.151 | - |
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+ | 0.0320 | 750 | 0.1034 | - |
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+ | 0.0341 | 800 | 0.2356 | - |
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+ | 0.0363 | 850 | 0.1335 | - |
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+ | 0.0384 | 900 | 0.0559 | - |
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+ | 0.0405 | 950 | 0.0028 | - |
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+ | 0.0427 | 1000 | 0.1307 | - |
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+ | 0.0448 | 1050 | 0.0049 | - |
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+ | 0.0469 | 1100 | 0.1348 | - |
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+ | 0.0491 | 1150 | 0.0392 | - |
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+ | 0.0512 | 1200 | 0.054 | - |
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+ | 0.0533 | 1250 | 0.0016 | - |
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+ | 0.0555 | 1300 | 0.0012 | - |
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+ | 0.0576 | 1350 | 0.0414 | - |
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+ | 0.0597 | 1400 | 0.1087 | - |
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+ | 0.0618 | 1450 | 0.0464 | - |
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+ | 0.0640 | 1500 | 0.0095 | - |
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+ | 0.0661 | 1550 | 0.0011 | - |
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+ | 0.0682 | 1600 | 0.0002 | - |
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+ | 0.0704 | 1650 | 0.1047 | - |
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+ | 0.0725 | 1700 | 0.001 | - |
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+ | 0.0746 | 1750 | 0.0965 | - |
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+ | 0.0768 | 1800 | 0.0002 | - |
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+ | 0.0789 | 1850 | 0.1436 | - |
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+ | 0.0810 | 1900 | 0.0011 | - |
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+ | 0.0832 | 1950 | 0.001 | - |
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+ | 0.0853 | 2000 | 0.1765 | - |
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+ | 0.0874 | 2050 | 0.1401 | - |
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+ | 0.0896 | 2100 | 0.0199 | - |
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+ | 0.0917 | 2150 | 0.0 | - |
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+ | 0.0938 | 2200 | 0.0023 | - |
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+ | 0.0960 | 2250 | 0.0034 | - |
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+ | 0.0981 | 2300 | 0.0001 | - |
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+ | 0.1002 | 2350 | 0.0948 | - |
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+ | 0.1024 | 2400 | 0.1634 | - |
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+ | 0.1045 | 2450 | 0.0 | - |
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+ | 0.1066 | 2500 | 0.0005 | - |
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+ | 0.1088 | 2550 | 0.0695 | - |
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+ | 0.1109 | 2600 | 0.0 | - |
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+ | 0.1130 | 2650 | 0.0067 | - |
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+ | 0.1152 | 2700 | 0.0025 | - |
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+ | 0.1173 | 2750 | 0.0013 | - |
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+ | 0.1194 | 2800 | 0.1426 | - |
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+ | 0.1216 | 2850 | 0.0001 | - |
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+ | 0.1237 | 2900 | 0.0 | - |
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+ | 0.1258 | 2950 | 0.0 | - |
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+ | 0.1280 | 3000 | 0.0001 | - |
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+ | 0.1301 | 3050 | 0.0001 | - |
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+ | 0.1322 | 3100 | 0.0122 | - |
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+ | 0.1344 | 3150 | 0.0 | - |
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+ | 0.1365 | 3200 | 0.0001 | - |
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+ | 0.1386 | 3250 | 0.0041 | - |
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+ | 0.1408 | 3300 | 0.2549 | - |
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+ | 0.1429 | 3350 | 0.0062 | - |
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+ | 0.1450 | 3400 | 0.0154 | - |
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+ | 0.1472 | 3450 | 0.1776 | - |
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+ | 0.1493 | 3500 | 0.0039 | - |
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+ | 0.1514 | 3550 | 0.0183 | - |
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+ | 0.1536 | 3600 | 0.0045 | - |
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+ | 0.1557 | 3650 | 0.1108 | - |
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+ | 0.1578 | 3700 | 0.0002 | - |
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+ | 0.1600 | 3750 | 0.01 | - |
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+ | 0.1621 | 3800 | 0.0002 | - |
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+ | 0.1642 | 3850 | 0.0001 | - |
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+ | 0.1664 | 3900 | 0.1612 | - |
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+ | 0.1685 | 3950 | 0.0107 | - |
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+ | 0.1706 | 4000 | 0.0548 | - |
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+ | 0.1728 | 4050 | 0.0001 | - |
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+ | 0.1749 | 4100 | 0.0162 | - |
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+ | 0.1770 | 4150 | 0.1294 | - |
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+ | 0.1792 | 4200 | 0.0 | - |
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+ | 0.1813 | 4250 | 0.0032 | - |
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+ | 0.1834 | 4300 | 0.0051 | - |
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+ | 0.1855 | 4350 | 0.0 | - |
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+ | 0.1877 | 4400 | 0.0151 | - |
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+ | 0.1898 | 4450 | 0.0097 | - |
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+ | 0.1919 | 4500 | 0.0002 | - |
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+ | 0.1941 | 4550 | 0.0045 | - |
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+ | 0.1962 | 4600 | 0.0001 | - |
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+ | 0.1983 | 4650 | 0.0001 | - |
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+ | 0.2005 | 4700 | 0.0227 | - |
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+ | 0.2026 | 4750 | 0.0018 | - |
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+ | 0.2047 | 4800 | 0.0 | - |
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+ | 0.2069 | 4850 | 0.0001 | - |
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+ | 0.2090 | 4900 | 0.0 | - |
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+ | 0.2111 | 4950 | 0.0 | - |
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+ | 0.2133 | 5000 | 0.0 | - |
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+ | 0.2154 | 5050 | 0.0002 | - |
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+ | 0.2175 | 5100 | 0.0002 | - |
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+ | 0.2197 | 5150 | 0.0038 | - |
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+ | 0.2218 | 5200 | 0.0 | - |
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+ | 0.2239 | 5250 | 0.0 | - |
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+ | 0.2261 | 5300 | 0.0 | - |
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+ | 0.2282 | 5350 | 0.0028 | - |
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+ | 0.2303 | 5400 | 0.0 | - |
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+ | 0.2325 | 5450 | 0.1146 | - |
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+ | 0.2346 | 5500 | 0.0 | - |
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+ | 0.2367 | 5550 | 0.0073 | - |
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+ | 0.2389 | 5600 | 0.0467 | - |
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+ | 0.2410 | 5650 | 0.0092 | - |
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+ | 0.2431 | 5700 | 0.0196 | - |
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+ | 0.2453 | 5750 | 0.0002 | - |
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+ | 0.2474 | 5800 | 0.0043 | - |
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+ | 0.2495 | 5850 | 0.0378 | - |
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+ | 0.2517 | 5900 | 0.0049 | - |
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+ | 0.2538 | 5950 | 0.0054 | - |
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+ | 0.2559 | 6000 | 0.1757 | - |
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+ | 0.2581 | 6050 | 0.0 | - |
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+ | 0.2602 | 6100 | 0.0001 | - |
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+ | 0.2623 | 6150 | 0.1327 | - |
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+ | 0.2645 | 6200 | 0.0 | - |
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+ | 0.2666 | 6250 | 0.0 | - |
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+ | 0.2687 | 6300 | 0.0 | - |
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+ | 0.2709 | 6350 | 0.0134 | - |
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+ | 0.2730 | 6400 | 0.0001 | - |
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+ | 0.2751 | 6450 | 0.0112 | - |
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+ | 0.2773 | 6500 | 0.0864 | - |
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+ | 0.2794 | 6550 | 0.0 | - |
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+ | 0.2815 | 6600 | 0.0094 | - |
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+ | 0.2837 | 6650 | 0.1358 | - |
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+ | 0.2858 | 6700 | 0.0155 | - |
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+ | 0.2879 | 6750 | 0.0025 | - |
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+ | 0.2901 | 6800 | 0.0002 | - |
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+ | 0.2922 | 6850 | 0.0001 | - |
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+ | 0.2943 | 6900 | 0.2809 | - |
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+ | 0.2965 | 6950 | 0.0 | - |
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+ | 0.2986 | 7000 | 0.0242 | - |
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+ | 0.3007 | 7050 | 0.0015 | - |
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+ | 0.3028 | 7100 | 0.0 | - |
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+ | 0.3050 | 7150 | 0.1064 | - |
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+ | 0.3071 | 7200 | 0.1636 | - |
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+ | 0.3092 | 7250 | 0.267 | - |
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+ | 0.3114 | 7300 | 0.1656 | - |
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+ | 0.3135 | 7350 | 0.0943 | - |
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+ | 0.3156 | 7400 | 0.189 | - |
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+ | 0.3178 | 7450 | 0.0055 | - |
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+ | 0.3199 | 7500 | 0.1286 | - |
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+ | 0.3220 | 7550 | 0.1062 | - |
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+ | 0.3242 | 7600 | 0.1275 | - |
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+ | 0.3263 | 7650 | 0.0101 | - |
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+ | 0.3284 | 7700 | 0.0162 | - |
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+ | 0.3306 | 7750 | 0.0001 | - |
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+ | 0.3327 | 7800 | 0.0001 | - |
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+ | 0.3348 | 7850 | 0.0003 | - |
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+ | 0.3370 | 7900 | 0.0 | - |
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+ | 0.3391 | 7950 | 0.135 | - |
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+ | 0.3412 | 8000 | 0.0 | - |
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+ | 0.3434 | 8050 | 0.0125 | - |
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+ | 0.3455 | 8100 | 0.0004 | - |
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+ | 0.3476 | 8150 | 0.0 | - |
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+ | 0.3498 | 8200 | 0.2229 | - |
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+ | 0.3519 | 8250 | 0.0 | - |
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+ | 0.3540 | 8300 | 0.0051 | - |
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+ | 0.3562 | 8350 | 0.0 | - |
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+ | 0.3583 | 8400 | 0.0001 | - |
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+ | 0.3604 | 8450 | 0.0 | - |
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+ | 0.3626 | 8500 | 0.1261 | - |
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+ | 0.3647 | 8550 | 0.0054 | - |
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+ | 0.3668 | 8600 | 0.1636 | - |
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+ | 0.3690 | 8650 | 0.0036 | - |
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+ | 0.3711 | 8700 | 0.0 | - |
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+ | 0.3732 | 8750 | 0.0027 | - |
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+ | 0.3754 | 8800 | 0.0 | - |
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+ | 0.3775 | 8850 | 0.1422 | - |
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+ | 0.3796 | 8900 | 0.1314 | - |
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+ | 0.3818 | 8950 | 0.003 | - |
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+ | 0.3839 | 9000 | 0.0 | - |
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+ | 0.3860 | 9050 | 0.0092 | - |
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+ | 0.3882 | 9100 | 0.0129 | - |
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+ | 0.3903 | 9150 | 0.0 | - |
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+ | 0.3924 | 9200 | 0.0 | - |
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+ | 0.3946 | 9250 | 0.1659 | - |
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+ | 0.3967 | 9300 | 0.0 | - |
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+
643
+ ### Framework Versions
644
+ - Python: 3.10.13
645
+ - SetFit: 1.0.3
646
+ - Sentence Transformers: 2.7.0
647
+ - spaCy: 3.7.4
648
+ - Transformers: 4.36.2
649
+ - PyTorch: 2.1.2
650
+ - Datasets: 2.18.0
651
+ - Tokenizers: 0.15.2
652
+
653
+ ## Citation
654
+
655
+ ### BibTeX
656
+ ```bibtex
657
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
658
+ doi = {10.48550/ARXIV.2209.11055},
659
+ url = {https://arxiv.org/abs/2209.11055},
660
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
661
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
662
+ title = {Efficient Few-Shot Learning Without Prompts},
663
+ publisher = {arXiv},
664
+ year = {2022},
665
+ copyright = {Creative Commons Attribution 4.0 International}
666
+ }
667
+ ```
668
+
669
+ <!--
670
+ ## Glossary
671
+
672
+ *Clearly define terms in order to be accessible across audiences.*
673
+ -->
674
+
675
+ <!--
676
+ ## Model Card Authors
677
+
678
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
679
+ -->
680
+
681
+ <!--
682
+ ## Model Card Contact
683
+
684
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
685
+ -->
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@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "cls_token": {
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+ "content": "[CLS]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "mask_token": {
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+ "content": "[MASK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "single_word": false
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+ },
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+ "pad_token": {
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+ "content": "[PAD]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "sep_token": {
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+ "content": "[SEP]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "unk_token": {
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+ "content": "[UNK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ }
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+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "added_tokens_decoder": {
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+ "0": {
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+ "content": "[PAD]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "1": {
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+ "content": "[UNK]",
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+ "single_word": false,
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+ "special": true
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+ },
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+ "2": {
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+ "content": "[CLS]",
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+ "single_word": false,
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+ "special": true
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+ },
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+ "3": {
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "4": {
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+ "content": "[MASK]",
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ }
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+ },
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+ "clean_up_tokenization_spaces": true,
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+ "cls_token": "[CLS]",
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+ "do_basic_tokenize": true,
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+ "do_lower_case": true,
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+ "mask_token": "[MASK]",
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+ "max_length": 512,
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+ "model_max_length": 1000000000000000019884624838656,
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+ "never_split": null,
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+ "pad_to_multiple_of": null,
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+ "pad_token": "[PAD]",
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+ "pad_token_type_id": 0,
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+ "padding_side": "right",
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+ "sep_token": "[SEP]",
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+ "stride": 0,
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+ "strip_accents": null,
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+ "tokenize_chinese_chars": true,
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+ "tokenizer_class": "BertTokenizer",
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+ "truncation_side": "right",
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+ "truncation_strategy": "longest_first",
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+ "unk_token": "[UNK]"
64
+ }
vocab.txt ADDED
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