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Push model using huggingface_hub.

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README.md ADDED
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+ ---
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ widget:
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+ - text: 하이라이크 유모차장갑 핸드머프 방한장갑 블랙체스 출산/육아 > 유모차 > 유모차용품 > 기타유모차용품
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+ - text: 대통 K-Express 대문 출입문 게이트 스텐 펜스 접이식 정문 자바라 절연 텔레스코픽 튜브 울타리 전기 안전 건설 이동식 난간 유치원
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+ 격리 AB.플라스틱 0.96 높이X2.5 긴 빨간색 출산/육아 > 유모차 > 유모차용품 > 유모차보호대/안전바
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+ - text: HABBY 휴대용/절충형/디럭스 유모차 누빔 방한커버 방풍커버 04.하삐 휴대용 방한커버 출산/육아 > 유모차 > 유모차용품 > 유모차커버
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+ - text: 올겟쇼핑 나이스 유모차 컵홀더 스마트폰거치대 출산/육아 > 유모차 > 유모차용품 > 유모차홀더
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+ - text: NEW 오이스터3 플러스 유모차 클래식 에디션 브라운 샌드 베이지 디럭스 절충형 8종선물 오이스터3 플러스 에디션_플러스 샴페인샌드(8종선물)
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+ 출산/육아 > 유모차 > 절충형/디럭스형
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ library_name: setfit
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+ inference: true
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+ base_model: mini1013/master_domain
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+ model-index:
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+ - name: SetFit with mini1013/master_domain
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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: 1.0
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with mini1013/master_domain
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) 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.
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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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+ ## 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:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
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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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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 5 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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+ | 4.0 | <ul><li>'스토케 베이비젠 요요2 프리미엄 휴대용 유모차 화이트_올리브 출산/육아 > 유모차 > 초경량/휴대용'</li><li>'이지폴드3 하이브리드 초경량 오토폴딩 휴대용 유모차 - 아미그린 크림베이지 출산/육아 > 유모차 > 초경량/휴대용'</li><li>'르클레르 인플루언서 에디션 휴대용 유모차 - 샌드쇼콜라 [선물6종] 제트블랙 출산/육아 > 유모차 > 초경량/휴대용'</li></ul> |
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+ | 2.0 | <ul><li>'유아목베개 유모차베개 카시트 목쿠션 01_딸기옐로우 매쉬목베개 출산/육아 > 유모차 > 유모차용품 > 유모차목쿠션/블랭킷'</li><li>'돗투돗 소프트 유모차 라이너 유모차 시트 신생아 시트 100수소프트라이너_베이지덕 출산/육아 > 유모차 > 유모차용품 > 유모차시트'</li><li>'아기 방수 겨울용 유모차 침낭 따뜻한 발싸개 범용 풋커버 풋머프 슬립 색 봉투 15=6-36mGray 출산/육아 > 유모차 > 유모차용품 > 유모차시트'</li></ul> |
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+ | 3.0 | <ul><li>'[디럭스]부가부 폭스5 그래파이트 섀시/미드나이트 블랙 시트패브릭_100052034 포레스트 그린 출산/육아 > 유모차 > 절충형/디럭스형'</li><li>'마마스앤파파스 절충형 유모차 스트라다 - 그레이 아이비_컵홀더/모기장/레인커버/맘노코방풍커버 출산/육아 > 유모차 > 절충형/디럭스형'</li><li>'[판매] 싸이벡스 프리암 맨하탄그레이 디럭스 유모차 비동의_프리암 맨하탄그레이 출산/육아 > 유모차 > 절충형/디럭스형'</li></ul> |
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+ | 0.0 | <ul><li>'스토케 베이비젠 요요2 프리미엄 휴대용 유모차(커넥트 포함) 쌍둥이 유모차 블랙_에어프랑스 블루_타프 출산/육아 > 유모차 > 쌍둥이용'</li><li>'형제유모차 쌍둥이유모차 더블시트 디럭스형 폴딩 색상 유형 및 구성13 출산/육아 > 유모차 > 쌍둥이용'</li><li>'트윈유모차 2인용 휴대용유모카 디럭스형 트라이크 디자인E 출산/육아 > 유모차 > 쌍둥이용'</li></ul> |
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+ | 1.0 | <ul><li>'원클릭으로 앉거나 누울 수 있는 유모차, 초경량 충격흡수 접이식 간편 신생아 유모차 12 주력 모델 기질 파란색 원클릭으로 차 출산/육아 > 유모차 > 유모차/카시트세트'</li><li>'어린이카시트 쥬니어카시트 유아용 휴대용 간단한 잠금 해제 도구 자동차 좌석 키 장치, 안 03 C 출산/육아 > 유모차 > 유모차/카시트세트'</li><li>'유모차 유물, 경량 접이식 어린이 유모차, 양방향 유모차, 아기 산책 시 원클릭 접이식 15 카라멜 컬러 자동차 매트시트 수납백팩 출산/육아 > 유모차 > 유모차/카시트세트'</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** | 1.0 |
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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 SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("mini1013/master_cate_bc16")
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+ # Run inference
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+ preds = model("올겟쇼핑 나이스 유모차 컵홀더 스마트폰거치대 출산/육아 > 유모차 > 유모차용품 > 유모차홀더")
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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 | 7 | 16.2771 | 32 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0.0 | 70 |
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+ | 1.0 | 70 |
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+ | 2.0 | 70 |
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+ | 3.0 | 70 |
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+ | 4.0 | 70 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (256, 256)
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+ - num_epochs: (30, 30)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 50
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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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+ - l2_weight: 0.01
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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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+ | 0.0145 | 1 | 0.4827 | - |
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+ | 0.7246 | 50 | 0.4996 | - |
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+ | 1.4493 | 100 | 0.4912 | - |
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+ | 2.1739 | 150 | 0.2633 | - |
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+ | 2.8986 | 200 | 0.0252 | - |
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+ | 3.6232 | 250 | 0.0001 | - |
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+ | 4.3478 | 300 | 0.0 | - |
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+ | 5.0725 | 350 | 0.0 | - |
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+ | 5.7971 | 400 | 0.0 | - |
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+ | 6.5217 | 450 | 0.0 | - |
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+ | 7.2464 | 500 | 0.0 | - |
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+ | 7.9710 | 550 | 0.0 | - |
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+ | 8.6957 | 600 | 0.0 | - |
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+ | 9.4203 | 650 | 0.0 | - |
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+ | 10.1449 | 700 | 0.0 | - |
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+ | 10.8696 | 750 | 0.0 | - |
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+ | 11.5942 | 800 | 0.0 | - |
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+ | 12.3188 | 850 | 0.0 | - |
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+ | 13.0435 | 900 | 0.0 | - |
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+ | 13.7681 | 950 | 0.0 | - |
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+ | 14.4928 | 1000 | 0.0 | - |
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+ | 15.2174 | 1050 | 0.0 | - |
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+ | 15.9420 | 1100 | 0.0 | - |
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+ | 16.6667 | 1150 | 0.0 | - |
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+ | 17.3913 | 1200 | 0.0 | - |
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+ | 18.1159 | 1250 | 0.0 | - |
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+ | 18.8406 | 1300 | 0.0 | - |
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+ | 19.5652 | 1350 | 0.0 | - |
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+ | 20.2899 | 1400 | 0.0 | - |
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+ | 21.0145 | 1450 | 0.0 | - |
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+ | 21.7391 | 1500 | 0.0 | - |
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+ | 22.4638 | 1550 | 0.0 | - |
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+ | 23.1884 | 1600 | 0.0 | - |
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+ | 23.9130 | 1650 | 0.0 | - |
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+ | 24.6377 | 1700 | 0.0 | - |
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+ | 25.3623 | 1750 | 0.0 | - |
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+ | 26.0870 | 1800 | 0.0 | - |
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+ | 26.8116 | 1850 | 0.0 | - |
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+ | 27.5362 | 1900 | 0.0 | - |
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+ | 28.2609 | 1950 | 0.0 | - |
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+ | 28.9855 | 2000 | 0.0 | - |
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+ | 29.7101 | 2050 | 0.0 | - |
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+
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+ ### Framework Versions
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+ - Python: 3.10.12
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+ - SetFit: 1.1.0
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+ - Sentence Transformers: 3.3.1
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+ - Transformers: 4.44.2
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+ - PyTorch: 2.2.0a0+81ea7a4
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+ - Datasets: 3.2.0
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+ - Tokenizers: 0.19.1
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+
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+ ## Citation
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+
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+ ### BibTeX
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+ ```bibtex
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+ @article{https://doi.org/10.48550/arxiv.2209.11055,
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+ doi = {10.48550/ARXIV.2209.11055},
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+ url = {https://arxiv.org/abs/2209.11055},
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+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
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+ publisher = {arXiv},
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+ 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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+ }
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special_tokens_map.json ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "bos_token": {
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+ "content": "[CLS]",
4
+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
8
+ },
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+ "cls_token": {
10
+ "content": "[CLS]",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "eos_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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+ "mask_token": {
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+ "content": "[MASK]",
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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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+ "pad_token": {
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+ "content": "[PAD]",
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+ "lstrip": false,
33
+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
36
+ },
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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,
47
+ "normalized": false,
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+ "rstrip": false,
49
+ "single_word": false
50
+ }
51
+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "added_tokens_decoder": {
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+ "0": {
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+ "content": "[CLS]",
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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": "[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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+ "2": {
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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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+ "special": true
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+ },
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+ "3": {
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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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+ "special": true
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+ },
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+ "4": {
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+ "content": "[MASK]",
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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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+ },
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+ "bos_token": "[CLS]",
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+ "clean_up_tokenization_spaces": false,
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+ "cls_token": "[CLS]",
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+ "do_basic_tokenize": true,
48
+ "do_lower_case": false,
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+ "eos_token": "[SEP]",
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+ "mask_token": "[MASK]",
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+ "max_length": 512,
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+ "model_max_length": 512,
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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",
63
+ "truncation_side": "right",
64
+ "truncation_strategy": "longest_first",
65
+ "unk_token": "[UNK]"
66
+ }
vocab.txt ADDED
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