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/
RevenueStreamJP

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Model card Files Community
MaiDD commited on
Commit
73913dc
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1 Parent(s): 7df54c7

Add SetFit model

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  1. README.md +253 -214
  2. model.safetensors +1 -1
  3. model_head.pkl +1 -1
README.md CHANGED
@@ -8,12 +8,11 @@ tags:
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  metrics:
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  - accuracy
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  widget:
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- - text: 多要素認証エンジンである「LOCKED」と、セキュリティコンサルティングを通じて、国内企業のゼロトラスト対応を支援しているスタートアップ。
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- - text: Hotel rooms on the wheelsをコンセプトにした、自社生産のキャンピングカーレンタルサービスを展開するスタートアップ。
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- - text: バイオ新薬事業やバイオシミラー事業などバイオに関わる研究開発を行う企業。2021年7月にジーンテクノサイエンスからキッズウェル・バイオに社名変更をしている。
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- - text: 業務用冷凍食品の企画・開発・販売を行い、自社商品の調理方法などを公開する企業。
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- - text: がん治療機器「集束超音波(HIFU)治療装置」の開発を行う東北大学発のスタートアップ。「集束超音波」は、超音波を一点に集中させてがん組織に照射し、加熱効果などで切らずに治療する方法。放射線被曝が無いことから繰り返し治療ができ、がんに対する次世代治療として期待されている。2022年12月には、ニッセイ・キャピタル、野村スパークス・インベストメント、大和企業投資、りそなキャピタル、Carbon
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- Ventures、QRインベストメント、JA三井リース、ファストトラックイニシアティブ、SBIインベストメント、三菱UFJキャピタル、FFGベンチャービジネスパートナーズ、肥銀キャピタルを引受先とする総額23億5,000万円の資金調達を発表した。今後は、膵癌の国内治験および海外展開を含めた事業拡大に充当し、同社のビジョンである“音響工学(超音波)でがん患者さんに新たな未来をもたらす”を1日でも早く実現することを目指す。
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  pipeline_tag: text-classification
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  inference: false
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  model-index:
@@ -28,7 +27,7 @@ model-index:
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  split: test
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  metrics:
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  - type: accuracy
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- value: 0.7902097902097902
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  name: Accuracy
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  ---
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@@ -64,7 +63,7 @@ The model has been trained using an efficient few-shot learning technique that i
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  ### Metrics
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  | Label | Accuracy |
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  |:--------|:---------|
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- | **all** | 0.7902 |
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  ## Uses
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@@ -84,7 +83,7 @@ from setfit import SetFitModel
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("Ekohe/RevenueStreamJP")
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  # Run inference
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- preds = model("業務用冷凍食品の企画・開発・販売を行い、自社商品の調理方法などを公開する企業。")
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  ```
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  <!--
@@ -116,14 +115,14 @@ preds = model("業務用冷凍食品の企画・開発・販売を行い、自
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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 | 1 | 1.8981 | 57 |
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121
  ### Training Hyperparameters
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- - batch_size: (8, 8)
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  - num_epochs: (35, 35)
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  - max_steps: -1
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  - sampling_strategy: oversampling
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- - num_iterations: 2
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  - body_learning_rate: (2e-05, 2e-05)
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  - head_learning_rate: 2e-05
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  - loss: CosineSimilarityLoss
@@ -137,208 +136,248 @@ preds = model("業務用冷凍食品の企画・開発・販売を行い、自
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  - load_best_model_at_end: False
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  ### Training Results
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- | Epoch | Step | Training Loss | Validation Loss |
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- |:-------:|:----:|:-------------:|:---------------:|
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- | 0.0035 | 1 | 0.3068 | - |
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- | 0.1754 | 50 | 0.2708 | - |
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- | 0.3509 | 100 | 0.2253 | - |
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- | 0.5263 | 150 | 0.2705 | - |
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- | 0.7018 | 200 | 0.1665 | - |
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- | 0.8772 | 250 | 0.2609 | - |
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- | 1.0526 | 300 | 0.2681 | - |
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- | 1.2281 | 350 | 0.2614 | - |
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- | 1.4035 | 400 | 0.2151 | - |
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- | 1.5789 | 450 | 0.1952 | - |
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- | 1.7544 | 500 | 0.2275 | - |
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- | 1.9298 | 550 | 0.3111 | - |
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- | 2.1053 | 600 | 0.1036 | - |
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- | 2.2807 | 650 | 0.1038 | - |
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- | 2.4561 | 700 | 0.0081 | - |
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- | 2.6316 | 750 | 0.0906 | - |
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- | 2.8070 | 800 | 0.0002 | - |
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- | 2.9825 | 850 | 0.0928 | - |
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- | 3.1579 | 900 | 0.0004 | - |
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- | 3.3333 | 950 | 0.0011 | - |
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- | 3.5088 | 1000 | 0.0013 | - |
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- | 3.6842 | 1050 | 0.0004 | - |
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- | 3.8596 | 1100 | 0.0012 | - |
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- | 4.0351 | 1150 | 0.0002 | - |
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- | 4.2105 | 1200 | 0.0004 | - |
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- | 4.3860 | 1250 | 0.0003 | - |
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- | 4.5614 | 1300 | 0.0 | - |
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- | 4.7368 | 1350 | 0.0001 | - |
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- | 4.9123 | 1400 | 0.0002 | - |
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- | 5.0877 | 1450 | 0.0 | - |
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- | 5.2632 | 1500 | 0.0002 | - |
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- | 5.4386 | 1550 | 0.0 | - |
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- | 5.6140 | 1600 | 0.0 | - |
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- | 5.7895 | 1650 | 0.0 | - |
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- | 5.9649 | 1700 | 0.1017 | - |
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- | 6.1404 | 1750 | 0.0012 | - |
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- | 6.3158 | 1800 | 0.0 | - |
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- | 6.4912 | 1850 | 0.0001 | - |
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- | 6.6667 | 1900 | 0.0 | - |
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- | 6.8421 | 1950 | 0.0003 | - |
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- | 7.0175 | 2000 | 0.0 | - |
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- | 7.1930 | 2050 | 0.0 | - |
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- | 7.3684 | 2100 | 0.0 | - |
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- | 7.5439 | 2150 | 0.0 | - |
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- | 7.7193 | 2200 | 0.0 | - |
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- | 7.8947 | 2250 | 0.0 | - |
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- | 8.0702 | 2300 | 0.0 | - |
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- | 8.2456 | 2350 | 0.0 | - |
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- | 8.4211 | 2400 | 0.0019 | - |
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- | 8.5965 | 2450 | 0.0017 | - |
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- | 8.7719 | 2500 | 0.0 | - |
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- | 8.9474 | 2550 | 0.0034 | - |
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- | 9.1228 | 2600 | 0.0 | - |
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- | 9.2982 | 2650 | 0.0 | - |
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- | 9.4737 | 2700 | 0.0 | - |
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- | 9.6491 | 2750 | 0.0 | - |
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- | 9.8246 | 2800 | 0.0 | - |
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- | 10.0 | 2850 | 0.0 | - |
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- | 10.1754 | 2900 | 0.0 | - |
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- | 10.3509 | 2950 | 0.0 | - |
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- | 10.5263 | 3000 | 0.0 | - |
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- | 10.7018 | 3050 | 0.0 | - |
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- | 10.8772 | 3100 | 0.0001 | - |
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- | 11.0526 | 3150 | 0.0 | - |
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- | 11.2281 | 3200 | 0.0 | - |
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- | 11.4035 | 3250 | 0.0 | - |
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- | 11.5789 | 3300 | 0.0 | - |
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- | 11.7544 | 3350 | 0.0 | - |
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- | 11.9298 | 3400 | 0.0 | - |
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- | 12.1053 | 3450 | 0.0 | - |
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- | 12.2807 | 3500 | 0.0 | - |
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- | 12.4561 | 3550 | 0.0 | - |
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- | 12.6316 | 3600 | 0.0 | - |
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- | 12.8070 | 3650 | 0.0 | - |
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- | 12.9825 | 3700 | 0.0 | - |
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- | 13.1579 | 3750 | 0.0 | - |
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- | 13.3333 | 3800 | 0.0 | - |
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- | 13.5088 | 3850 | 0.0 | - |
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- | 13.6842 | 3900 | 0.0 | - |
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- | 13.8596 | 3950 | 0.0 | - |
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- | 14.0351 | 4000 | 0.0 | - |
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- | 14.2105 | 4050 | 0.0 | - |
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- | 14.3860 | 4100 | 0.0 | - |
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- | 14.5614 | 4150 | 0.0 | - |
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- | 14.7368 | 4200 | 0.0 | - |
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- | 14.9123 | 4250 | 0.0 | - |
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- | 15.0877 | 4300 | 0.0 | - |
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- | 15.2632 | 4350 | 0.0 | - |
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- | 15.4386 | 4400 | 0.0 | - |
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- | 15.6140 | 4450 | 0.0 | - |
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- | 15.7895 | 4500 | 0.0 | - |
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- | 15.9649 | 4550 | 0.1016 | - |
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- | 16.1404 | 4600 | 0.1214 | - |
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- | 16.3158 | 4650 | 0.0 | - |
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- | 16.4912 | 4700 | 0.0 | - |
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- | 16.6667 | 4750 | 0.0 | - |
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- | 16.8421 | 4800 | 0.0 | - |
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- | 17.0175 | 4850 | 0.0 | - |
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- | 17.1930 | 4900 | 0.0 | - |
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- | 17.3684 | 4950 | 0.0 | - |
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- | 17.5439 | 5000 | 0.0 | - |
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- | 17.7193 | 5050 | 0.0 | - |
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- | 17.8947 | 5100 | 0.0 | - |
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- | 18.0702 | 5150 | 0.0 | - |
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- | 18.2456 | 5200 | 0.0 | - |
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- | 18.4211 | 5250 | 0.0 | - |
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- | 18.5965 | 5300 | 0.0 | - |
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- | 18.7719 | 5350 | 0.0 | - |
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- | 18.9474 | 5400 | 0.0 | - |
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- | 19.1228 | 5450 | 0.0 | - |
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- | 19.2982 | 5500 | 0.0001 | - |
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- | 19.4737 | 5550 | 0.0 | - |
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- | 19.6491 | 5600 | 0.0001 | - |
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- | 19.8246 | 5650 | 0.0174 | - |
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- | 20.0 | 5700 | 0.0 | - |
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- | 20.1754 | 5750 | 0.0 | - |
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- | 20.3509 | 5800 | 0.0 | - |
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- | 20.5263 | 5850 | 0.0 | - |
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- | 20.7018 | 5900 | 0.0 | - |
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- | 20.8772 | 5950 | 0.0 | - |
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- | 21.0526 | 6000 | 0.0 | - |
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- | 21.2281 | 6050 | 0.0 | - |
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- | 21.4035 | 6100 | 0.0 | - |
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- | 21.5789 | 6150 | 0.0 | - |
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- | 21.7544 | 6200 | 0.0 | - |
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- | 21.9298 | 6250 | 0.0 | - |
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- | 22.1053 | 6300 | 0.0 | - |
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- | 22.2807 | 6350 | 0.0 | - |
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- | 22.4561 | 6400 | 0.0 | - |
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- | 22.6316 | 6450 | 0.0 | - |
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- | 22.8070 | 6500 | 0.0 | - |
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- | 22.9825 | 6550 | 0.0 | - |
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- | 23.1579 | 6600 | 0.0 | - |
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- | 23.3333 | 6650 | 0.0 | - |
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- | 23.5088 | 6700 | 0.0 | - |
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- | 23.6842 | 6750 | 0.0 | - |
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- | 23.8596 | 6800 | 0.0 | - |
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- | 24.0351 | 6850 | 0.0 | - |
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- | 24.2105 | 6900 | 0.0 | - |
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- | 24.3860 | 6950 | 0.0 | - |
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- | 24.5614 | 7000 | 0.0 | - |
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- | 24.7368 | 7050 | 0.0 | - |
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- | 24.9123 | 7100 | 0.0 | - |
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- | 25.0877 | 7150 | 0.0 | - |
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- | 25.2632 | 7200 | 0.0 | - |
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- | 25.4386 | 7250 | 0.0816 | - |
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- | 25.6140 | 7300 | 0.0005 | - |
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- | 25.7895 | 7350 | 0.0 | - |
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- | 25.9649 | 7400 | 0.0001 | - |
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- | 26.1404 | 7450 | 0.0001 | - |
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- | 26.3158 | 7500 | 0.0 | - |
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- | 26.4912 | 7550 | 0.0 | - |
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- | 26.6667 | 7600 | 0.0 | - |
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- | 26.8421 | 7650 | 0.0 | - |
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- | 27.0175 | 7700 | 0.0 | - |
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- | 27.1930 | 7750 | 0.0 | - |
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- | 27.3684 | 7800 | 0.0 | - |
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- | 27.5439 | 7850 | 0.0 | - |
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- | 27.7193 | 7900 | 0.0 | - |
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- | 27.8947 | 7950 | 0.0 | - |
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- | 28.0702 | 8000 | 0.0 | - |
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- | 28.2456 | 8050 | 0.0 | - |
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- | 28.4211 | 8100 | 0.0 | - |
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- | 28.5965 | 8150 | 0.0 | - |
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- | 28.7719 | 8200 | 0.0 | - |
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- | 28.9474 | 8250 | 0.0 | - |
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- | 29.1228 | 8300 | 0.0 | - |
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- | 29.2982 | 8350 | 0.0 | - |
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- | 29.4737 | 8400 | 0.0 | - |
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- | 29.6491 | 8450 | 0.0 | - |
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- | 29.8246 | 8500 | 0.0 | - |
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- | 30.0 | 8550 | 0.0 | - |
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- | 30.1754 | 8600 | 0.0 | - |
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- | 30.3509 | 8650 | 0.0 | - |
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- | 30.5263 | 8700 | 0.0 | - |
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- | 30.7018 | 8750 | 0.0 | - |
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- | 30.8772 | 8800 | 0.0 | - |
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- | 31.0526 | 8850 | 0.0 | - |
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- | 31.2281 | 8900 | 0.0 | - |
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- | 31.4035 | 8950 | 0.0 | - |
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- | 31.5789 | 9000 | 0.0 | - |
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- | 31.7544 | 9050 | 0.0 | - |
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- | 31.9298 | 9100 | 0.0 | - |
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- | 32.1053 | 9150 | 0.0 | - |
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- | 32.2807 | 9200 | 0.0 | - |
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- | 32.4561 | 9250 | 0.0 | - |
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- | 32.6316 | 9300 | 0.0 | - |
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- | 32.8070 | 9350 | 0.0 | - |
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- | 32.9825 | 9400 | 0.0 | - |
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- | 33.1579 | 9450 | 0.0 | - |
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- | 33.3333 | 9500 | 0.0 | - |
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- | 33.5088 | 9550 | 0.0 | - |
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- | 33.6842 | 9600 | 0.0 | - |
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- | 33.8596 | 9650 | 0.0 | - |
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- | 34.0351 | 9700 | 0.0 | - |
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- | 34.2105 | 9750 | 0.0 | - |
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- | 34.3860 | 9800 | 0.0 | - |
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- | 34.5614 | 9850 | 0.0 | - |
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- | 34.7368 | 9900 | 0.0 | - |
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- | 34.9123 | 9950 | 0.0 | - |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework Versions
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  - Python: 3.10.12
 
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  metrics:
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  - accuracy
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  widget:
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+ - text: スマホやタブレットPC、Oculus GOやVIVE、Apple Watchなど新しいデバイス向けアプリの企画・開発を行うスタートアップ。
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+ - text: ベンチャー企業へのハンズオン投資などを行うベンチャーキャピタル。
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+ - text: GoogleカレンダーやZoomと連携してスケジュール調整を自動化する日程調整ツール「Jicoo」を開発、提供するスタートアップ
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+ - text: 住まい探しに特化したウェブサイト「TOKYO APARTMENTS」を提供する企業。
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+ - text: 医療機器、産業機器の研究開発・製造販売を行う企業。
 
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  pipeline_tag: text-classification
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  inference: false
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  model-index:
 
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  split: test
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  metrics:
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  - type: accuracy
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+ value: 0.7272727272727273
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  name: Accuracy
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  ---
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  ### Metrics
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  | Label | Accuracy |
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  |:--------|:---------|
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+ | **all** | 0.7273 |
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  ## Uses
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  # Download from the 🤗 Hub
84
  model = SetFitModel.from_pretrained("Ekohe/RevenueStreamJP")
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  # Run inference
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+ preds = model("医療機器、産業機器の研究開発・製造販売を行う企業。")
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  ```
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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 | 1 | 1.9824 | 57 |
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  ### Training Hyperparameters
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+ - batch_size: (10, 10)
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  - num_epochs: (35, 35)
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  - max_steps: -1
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  - sampling_strategy: oversampling
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+ - num_iterations: 3
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  - body_learning_rate: (2e-05, 2e-05)
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  - head_learning_rate: 2e-05
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  - loss: CosineSimilarityLoss
 
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  - load_best_model_at_end: False
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  ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:-------:|:-----:|:-------------:|:---------------:|
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+ | 0.0029 | 1 | 0.2602 | - |
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+ | 0.1462 | 50 | 0.25 | - |
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+ | 0.2924 | 100 | 0.1712 | - |
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+ | 0.4386 | 150 | 0.2671 | - |
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+ | 0.5848 | 200 | 0.2288 | - |
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+ | 0.7310 | 250 | 0.2253 | - |
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+ | 0.8772 | 300 | 0.2675 | - |
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+ | 1.0234 | 350 | 0.1204 | - |
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+ | 1.1696 | 400 | 0.1185 | - |
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+ | 1.3158 | 450 | 0.1884 | - |
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+ | 1.4620 | 500 | 0.2311 | - |
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+ | 1.6082 | 550 | 0.0659 | - |
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+ | 1.7544 | 600 | 0.1719 | - |
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+ | 1.9006 | 650 | 0.0094 | - |
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+ | 2.0468 | 700 | 0.0237 | - |
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+ | 2.1930 | 750 | 0.0007 | - |
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+ | 2.3392 | 800 | 0.0021 | - |
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+ | 2.4854 | 850 | 0.0013 | - |
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+ | 2.6316 | 900 | 0.1887 | - |
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+ | 2.7778 | 950 | 0.0004 | - |
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+ | 2.9240 | 1000 | 0.0001 | - |
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+ | 3.0702 | 1050 | 0.0003 | - |
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+ | 3.2164 | 1100 | 0.0764 | - |
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+ | 3.3626 | 1150 | 0.0025 | - |
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+ | 3.5088 | 1200 | 0.0001 | - |
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+ | 3.6550 | 1250 | 0.0001 | - |
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+ | 3.8012 | 1300 | 0.0001 | - |
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+ | 3.9474 | 1350 | 0.0001 | - |
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+ | 4.0936 | 1400 | 0.0 | - |
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+ | 4.2398 | 1450 | 0.0001 | - |
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+ | 4.3860 | 1500 | 0.0001 | - |
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+ | 4.5322 | 1550 | 0.0 | - |
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+ | 4.6784 | 1600 | 0.0 | - |
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+ | 4.8246 | 1650 | 0.0 | - |
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+ | 4.9708 | 1700 | 0.0 | - |
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+ | 5.1170 | 1750 | 0.0001 | - |
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+ | 5.2632 | 1800 | 0.0 | - |
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+ | 5.4094 | 1850 | 0.0 | - |
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+ | 5.5556 | 1900 | 0.0 | - |
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+ | 5.7018 | 1950 | 0.0883 | - |
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+ | 5.8480 | 2000 | 0.0 | - |
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+ | 5.9942 | 2050 | 0.0 | - |
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+ | 6.1404 | 2100 | 0.0 | - |
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+ | 6.2865 | 2150 | 0.0 | - |
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+ | 6.4327 | 2200 | 0.0 | - |
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+ | 6.5789 | 2250 | 0.0 | - |
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+ | 6.7251 | 2300 | 0.0 | - |
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+ | 6.8713 | 2350 | 0.0 | - |
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+ | 7.0175 | 2400 | 0.0 | - |
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+ | 7.1637 | 2450 | 0.0 | - |
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+ | 7.3099 | 2500 | 0.0 | - |
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+ | 7.4561 | 2550 | 0.0 | - |
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+ | 7.6023 | 2600 | 0.0 | - |
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+ | 7.7485 | 2650 | 0.0 | - |
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+ | 7.8947 | 2700 | 0.0 | - |
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+ | 8.0409 | 2750 | 0.0 | - |
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+ | 8.1871 | 2800 | 0.0 | - |
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+ | 8.3333 | 2850 | 0.0 | - |
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+ | 8.4795 | 2900 | 0.0 | - |
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+ | 8.6257 | 2950 | 0.0 | - |
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+ | 8.7719 | 3000 | 0.0 | - |
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+ | 8.9181 | 3050 | 0.0 | - |
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+ | 9.0643 | 3100 | 0.0 | - |
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+ | 9.2105 | 3150 | 0.0 | - |
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+ | 9.3567 | 3200 | 0.0 | - |
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+ | 9.5029 | 3250 | 0.0618 | - |
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+ | 9.6491 | 3300 | 0.3522 | - |
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+ | 9.7953 | 3350 | 0.0051 | - |
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+ | 9.9415 | 3400 | 0.0002 | - |
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  ### Framework Versions
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