diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,1311 @@ +--- +library_name: setfit +tags: +- setfit +- sentence-transformers +- text-classification +- generated_from_setfit_trainer +base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 +metrics: +- accuracy +widget: +- text: Não apenas isso. A bola de neve do endividamento +- text: ' Bueno, yo lo que espero es que se traten con respeto, que se quieran. ' +- text: ' Sí, pues pedirle a María Luisa que le dé seguimiento y que siga atendiendo + las demandas de los ciudadanos de Vallarta, si te parece. Ya ella seguramente + nos está viendo y está tomando nota para darle continuidad a las demandas de + ambientalistas de Vallarta. ' +- text: A confiança na economia despertou o apetite pelo risco, criando instrumentos + financeiros indispensáveis à captação de novos recursos para a expansão produtiva. +- text: " A ver, pon la carta de Elba Esther. Es que luego la borró. Fue en mayo\ + \ del 23, 2 de mayo: ‘Ahí le espero con el Ejército —supuestamente esto\ + \ es lo que le dijo Calderón a la maestra Elba Esther, ahí la espero con el\ + \ Ejército— esa fue la respuesta del entonces presidente de México, Felipe\ + \ Calderón, cuando le dije —según la maestra— que las y los maestros de\ + \ México nos oponíamos a que Miguel Ã\x81ngel Yunes continuara como titular\ + \ del Issste, dadas las malversaciones de fondos financieros que con tanto trabajo\ + \ las los trabajadores al servicio del Estado logramos con la reforma a dicha\ + \ institución. ‘Cuando me comentó que Yunes estaba haciendo bien su trabajo,\ + \ no me dejó más alternativa —dice la maestra— que advertirle que tomaríamos\ + \ las instalaciones del Issste y justo esa fue su respuesta: Ahí la espero con\ + \ el Ejército. Esto sucedió en el marco de un evento público en una escuela\ + \ secundaria técnica de la ahora Ciudad de México. Ante su respuesta, me levanté\ + \ y me retiré. ‘Recordemos que la elección y remoción del director del Issste\ + \ compete única y exclusivamente al titular del Ejecutivo federal y no a una\ + \ servidora.’ Aquí me está contestando a mí, porque yo dije que a ella le\ + \ habían entregado por ayudar en el fraude, que no me diría la maestra que no\ + \ ayudó en el fraude del 2006, y a cambio yo sostengo que le entregaron el Issste,\ + \ la Subsecretaría de Educación Pública y la Lotería Nacional. ‘Por ello,\ + \ en relación a las declaraciones hechas por el presidente Andrés Manuel López\ + \ Obrador el pasado 29 de abril del presente año, sobre mi persona y la gestión\ + \ del señor Miguel Ã\x81ngel Yunes al frente del Issste, le digo categóricamente\ + \ que no participé el acto ilícito alguno, como me acusa desde su tribuna’.\ + \ Yo no estoy acusando más que de haberse aliado con Calderón y ayudarle en\ + \ el fraude electoral. ‘Siempre me he conducido conforme a derecho, de respeto\ + \ a las instituciones de este país y, desde luego, a la investidura presidencial.\ + \ Por ello, señor presidente, basta de falsas acusaciones a mi persona’. No\ + \ es nada personal, maestra, es que estamos viviendo un momento importantísimo\ + \ de transformación. Entonces, como el compañero que viene a hacernos preguntas\ + \ sobre salud, ayuda a recordar, porque es como si padecieran amnesia, ya se olvidó\ + \ cómo era. Y antes esto no lo tocaban, era silencio, como vasallos, obedecer\ + \ y callar, siempre y cuando hubiese dinero de por medio, porque lo que no suena\ + \ lógico suena metálico. Entonces, hay que ir aclarando todo, seguir purificando\ + \ la vida pública del país y por eso son muy buenas estas mañaneras. Pero,\ + \ bueno, eso es lo que queríamos decir. ¿Qué se está haciendo? Procurar, ya\ + \ es un compromiso, garantizar el derecho a la salud. Y vaya que ha costado, por\ + \ estos intereses. Imagínense, no se podían comprar medicinas en el extranjero\ + \ porque la ley lo prohibía, lo impedía; tuvimos que reformar la ley. ¿Y quiénes\ + \ votaron en contra de que se pudiera comprar la medicina en el extranjero? El\ + \ bloque conservador. ¿Qué son entonces? Representantes de minorías, no representantes\ + \ del pueblo, esa es nuestra diferencia de fondo. No es nada personal, pero sí\ + \ es importante el darle su sitio que le corresponde a lo público. República\ + \ es, res publica, cosa pública. Si vivimos en una república, tenemos que pensar\ + \ en eso, en lo público. Eso ya se había olvidado. Entonces, vamos a continuar\ + \ con lo mismo y va adelante todo el plan de transformación. El viernes vamos\ + \ a informar sobre salud y luego vamos a informar en específico sobre el Issste,\ + \ porque ya llevamos… ¿Cuánto tiempo llevamos? " +pipeline_tag: text-classification +inference: true +model-index: +- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 + results: + - task: + type: text-classification + name: Text Classification + dataset: + name: Unknown + type: unknown + split: test + metrics: + - type: accuracy + value: 0.7889908256880734 + name: Accuracy +--- + +# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 + +This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. + +The model has been trained using an efficient few-shot learning technique that involves: + +1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. +2. Training a classification head with features from the fine-tuned Sentence Transformer. + +## Model Details + +### Model Description +- **Model Type:** SetFit +- **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) +- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance +- **Maximum Sequence Length:** 128 tokens +- **Number of Classes:** 4 classes + + + + +### Model Sources + +- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) +- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) +- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co./blog/setfit) + +### Model Labels +| Label | Examples | +|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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+| 0 | | +| 1 | | +| 2 | | +| 3 | | + +## Evaluation + +### Metrics +| Label | Accuracy | +|:--------|:---------| +| **all** | 0.7890 | + +## Uses + +### Direct Use for Inference + +First install the SetFit library: + +```bash +pip install setfit +``` + +Then you can load this model and run inference. + +```python +from setfit import SetFitModel + +# Download from the 🤗 Hub +model = SetFitModel.from_pretrained("alelov/test-model-label2-MiniLMVERSION2") +# Run inference +preds = model("Não apenas isso. A bola de neve do endividamento") +``` + + + + + + + + + +## Training Details + +### Training Set Metrics +| Training set | Min | Median | Max | +|:-------------|:----|:---------|:-----| +| Word count | 1 | 103.4095 | 2340 | + +| Label | Training Sample Count | +|:------|:----------------------| +| 0 | 315 | +| 1 | 18 | +| 2 | 12 | +| 3 | 14 | + +### Training Hyperparameters +- batch_size: (16, 16) +- num_epochs: (4, 4) +- max_steps: -1 +- sampling_strategy: oversampling +- body_learning_rate: (2e-05, 1e-05) +- head_learning_rate: 0.01 +- loss: CosineSimilarityLoss +- distance_metric: cosine_distance +- margin: 0.25 +- end_to_end: False +- use_amp: False +- warmup_proportion: 0.1 +- seed: 42 +- eval_max_steps: -1 +- load_best_model_at_end: True + +### Training Results +| Epoch | Step | Training Loss | Validation Loss | +|:-------:|:--------:|:-------------:|:---------------:| +| 0.0002 | 1 | 0.3053 | - | +| 0.0080 | 50 | 0.3476 | - | +| 0.0160 | 100 | 0.3158 | - | +| 0.0239 | 150 | 0.3616 | - | +| 0.0319 | 200 | 0.2441 | - | +| 0.0399 | 250 | 0.265 | - | +| 0.0479 | 300 | 0.2206 | - | +| 0.0559 | 350 | 0.1637 | - | +| 0.0638 | 400 | 0.1088 | - | +| 0.0718 | 450 | 0.0766 | - | +| 0.0798 | 500 | 0.0297 | - | +| 0.0878 | 550 | 0.0709 | - | +| 0.0958 | 600 | 0.018 | - | +| 0.1037 | 650 | 0.0359 | - | +| 0.1117 | 700 | 0.0111 | - | +| 0.1197 | 750 | 0.0512 | - | +| 0.1277 | 800 | 0.0022 | - | +| 0.1357 | 850 | 0.0011 | - | +| 0.1436 | 900 | 0.0036 | - | +| 0.1516 | 950 | 0.0021 | - | +| 0.1596 | 1000 | 0.0515 | - | +| 0.1676 | 1050 | 0.0013 | - | +| 0.1756 | 1100 | 0.0193 | - | +| 0.1835 | 1150 | 0.0007 | - | +| 0.1915 | 1200 | 0.0072 | - | +| 0.1995 | 1250 | 0.0004 | - | +| 0.2075 | 1300 | 0.0005 | - | +| 0.2154 | 1350 | 0.0006 | - | +| 0.2234 | 1400 | 0.0014 | - | +| 0.2314 | 1450 | 0.0043 | - | +| 0.2394 | 1500 | 0.0009 | - | +| 0.2474 | 1550 | 0.0005 | - | +| 0.2553 | 1600 | 0.0003 | - | +| 0.2633 | 1650 | 0.0022 | - | +| 0.2713 | 1700 | 0.0037 | - | +| 0.2793 | 1750 | 0.0002 | - | +| 0.2873 | 1800 | 0.0009 | - | +| 0.2952 | 1850 | 0.0089 | - | +| 0.3032 | 1900 | 0.0003 | - | +| 0.3112 | 1950 | 0.001 | - | +| 0.3192 | 2000 | 0.0006 | - | +| 0.3272 | 2050 | 0.0005 | - | +| 0.3351 | 2100 | 0.0003 | - | +| 0.3431 | 2150 | 0.0414 | - | +| 0.3511 | 2200 | 0.0136 | - | +| 0.3591 | 2250 | 0.0003 | - | +| 0.3671 | 2300 | 0.0023 | - | +| 0.3750 | 2350 | 0.0002 | - | +| 0.3830 | 2400 | 0.0002 | - | +| 0.3910 | 2450 | 0.0047 | - | +| 0.0002 | 1 | 0.0094 | - | +| 0.0080 | 50 | 0.0002 | - | +| 0.0160 | 100 | 0.001 | - | +| 0.0239 | 150 | 0.0001 | - | +| 0.0319 | 200 | 0.0001 | - | +| 0.0399 | 250 | 0.0003 | - | +| 0.0479 | 300 | 0.0001 | - | +| 0.0559 | 350 | 0.0001 | - | +| 0.0638 | 400 | 0.0001 | - | +| 0.0718 | 450 | 0.0001 | - | +| 0.0798 | 500 | 0.0521 | - | +| 0.0878 | 550 | 0.0 | - | +| 0.0958 | 600 | 0.0003 | - | +| 0.1037 | 650 | 0.0011 | - | +| 0.1117 | 700 | 0.0001 | - | +| 0.1197 | 750 | 0.0006 | - | +| 0.1277 | 800 | 0.0006 | - | +| 0.1357 | 850 | 0.0 | - | +| 0.1436 | 900 | 0.0001 | - | +| 0.1516 | 950 | 0.0001 | - | +| 0.1596 | 1000 | 0.0016 | - | +| 0.1676 | 1050 | 0.0001 | - | +| 0.1756 | 1100 | 0.004 | - | +| 0.1835 | 1150 | 0.0 | - | +| 0.1915 | 1200 | 0.0001 | - | +| 0.1995 | 1250 | 0.002 | - | +| 0.2075 | 1300 | 0.0004 | - | +| 0.2154 | 1350 | 0.0002 | - | +| 0.2234 | 1400 | 0.0001 | - | +| 0.2314 | 1450 | 0.008 | - | +| 0.2394 | 1500 | 0.0001 | - | +| 0.2474 | 1550 | 0.0008 | - | +| 0.2553 | 1600 | 0.0001 | - | +| 0.2633 | 1650 | 0.0002 | - | +| 0.2713 | 1700 | 0.0005 | - | +| 0.2793 | 1750 | 0.0 | - | +| 0.2873 | 1800 | 0.0 | - | +| 0.2952 | 1850 | 0.0001 | - | +| 0.3032 | 1900 | 0.0 | - | +| 0.3112 | 1950 | 0.0 | - | +| 0.3192 | 2000 | 0.0002 | - | +| 0.3272 | 2050 | 0.0 | - | +| 0.3351 | 2100 | 0.0 | - | +| 0.3431 | 2150 | 0.0005 | - | +| 0.3511 | 2200 | 0.0008 | - | +| 0.3591 | 2250 | 0.0001 | - | +| 0.3671 | 2300 | 0.0004 | - | +| 0.3750 | 2350 | 0.0 | - | +| 0.3830 | 2400 | 0.0 | - | +| 0.3910 | 2450 | 0.0002 | - | +| 0.3990 | 2500 | 0.0 | - | +| 0.4070 | 2550 | 0.0 | - | +| 0.4149 | 2600 | 0.0001 | - | +| 0.4229 | 2650 | 0.0005 | - | +| 0.4309 | 2700 | 0.0 | - | +| 0.4389 | 2750 | 0.0002 | - | +| 0.4469 | 2800 | 0.0032 | - | +| 0.4548 | 2850 | 0.0008 | - | +| 0.4628 | 2900 | 0.0001 | - | +| 0.4708 | 2950 | 0.0001 | - | +| 0.4788 | 3000 | 0.0 | - | +| 0.4868 | 3050 | 0.0005 | - | +| 0.4947 | 3100 | 0.0 | - | +| 0.5027 | 3150 | 0.0001 | - | +| 0.5107 | 3200 | 0.0 | - | +| 0.5187 | 3250 | 0.0 | - | +| 0.5267 | 3300 | 0.0 | - | +| 0.5346 | 3350 | 0.0 | - | +| 0.5426 | 3400 | 0.0 | - | +| 0.5506 | 3450 | 0.0004 | - | +| 0.5586 | 3500 | 0.0 | - | +| 0.5665 | 3550 | 0.0001 | - | +| 0.5745 | 3600 | 0.0 | - | +| 0.5825 | 3650 | 0.0 | - | +| 0.5905 | 3700 | 0.0003 | - | +| 0.5985 | 3750 | 0.0 | - | +| 0.6064 | 3800 | 0.0001 | - | +| 0.6144 | 3850 | 0.0 | - | +| 0.6224 | 3900 | 0.0 | - | +| 0.6304 | 3950 | 0.0 | - | +| 0.6384 | 4000 | 0.0002 | - | +| 0.6463 | 4050 | 0.0001 | - | +| 0.6543 | 4100 | 0.0 | - | +| 0.6623 | 4150 | 0.0 | - | +| 0.6703 | 4200 | 0.0005 | - | +| 0.6783 | 4250 | 0.0 | - | +| 0.6862 | 4300 | 0.0 | - | +| 0.6942 | 4350 | 0.0002 | - | +| 0.7022 | 4400 | 0.0 | - | +| 0.7102 | 4450 | 0.0 | - | +| 0.7182 | 4500 | 0.0 | - | +| 0.7261 | 4550 | 0.0 | - | +| 0.7341 | 4600 | 0.0001 | - | +| 0.7421 | 4650 | 0.0 | - | +| 0.7501 | 4700 | 0.0 | - | +| 0.7581 | 4750 | 0.0 | - | +| 0.7660 | 4800 | 0.0 | - | +| 0.7740 | 4850 | 0.0675 | - | +| 0.7820 | 4900 | 0.0 | - | +| 0.7900 | 4950 | 0.0001 | - | +| 0.7980 | 5000 | 0.0 | - | +| 0.8059 | 5050 | 0.0 | - | +| 0.8139 | 5100 | 0.002 | - | +| 0.8219 | 5150 | 0.0003 | - | +| 0.8299 | 5200 | 0.0001 | - | +| 0.8379 | 5250 | 0.0003 | - | +| 0.8458 | 5300 | 0.0001 | - | +| 0.8538 | 5350 | 0.0 | - | +| 0.8618 | 5400 | 0.0 | - | +| 0.8698 | 5450 | 0.0 | - | +| 0.8778 | 5500 | 0.0 | - | +| 0.8857 | 5550 | 0.0 | - | +| 0.8937 | 5600 | 0.0 | - | +| 0.9017 | 5650 | 0.0 | - | +| 0.9097 | 5700 | 0.0001 | - | +| 0.9177 | 5750 | 0.0 | - | +| 0.9256 | 5800 | 0.0 | - | +| 0.9336 | 5850 | 0.0 | - | +| 0.9416 | 5900 | 0.0 | - | +| 0.9496 | 5950 | 0.0 | - | +| 0.9575 | 6000 | 0.0 | - | +| 0.9655 | 6050 | 0.0 | - | +| 0.9735 | 6100 | 0.0 | - | +| 0.9815 | 6150 | 0.0003 | - | +| 0.9895 | 6200 | 0.0 | - | +| 0.9974 | 6250 | 0.0 | - | +| **1.0** | **6266** | **-** | **0.2644** | +| 1.0054 | 6300 | 0.0 | - | +| 1.0134 | 6350 | 0.0 | - | +| 1.0214 | 6400 | 0.0 | - | +| 1.0294 | 6450 | 0.0 | - | +| 1.0373 | 6500 | 0.0 | - | +| 1.0453 | 6550 | 0.0004 | - | +| 1.0533 | 6600 | 0.0 | - | +| 1.0613 | 6650 | 0.0 | - | +| 1.0693 | 6700 | 0.0 | - | +| 1.0772 | 6750 | 0.0 | - | +| 1.0852 | 6800 | 0.0002 | - | +| 1.0932 | 6850 | 0.0 | - | +| 1.1012 | 6900 | 0.0 | - | +| 1.1092 | 6950 | 0.0 | - | +| 1.1171 | 7000 | 0.0 | - | +| 1.1251 | 7050 | 0.0 | - | +| 1.1331 | 7100 | 0.0 | - | +| 1.1411 | 7150 | 0.0 | - | +| 1.1491 | 7200 | 0.0 | - | +| 1.1570 | 7250 | 0.0 | - | +| 1.1650 | 7300 | 0.0 | - | +| 1.1730 | 7350 | 0.0 | - | +| 1.1810 | 7400 | 0.0 | - | +| 1.1890 | 7450 | 0.0 | - | +| 1.1969 | 7500 | 0.0423 | - | +| 1.2049 | 7550 | 0.0 | - | +| 1.2129 | 7600 | 0.0 | - | +| 1.2209 | 7650 | 0.0 | - | +| 1.2289 | 7700 | 0.0007 | - | +| 1.2368 | 7750 | 0.0 | - | +| 1.2448 | 7800 | 0.0 | - | +| 1.2528 | 7850 | 0.0001 | - | +| 1.2608 | 7900 | 0.0 | - | +| 1.2688 | 7950 | 0.0001 | - | +| 1.2767 | 8000 | 0.0 | - | +| 1.2847 | 8050 | 0.0 | - | +| 1.2927 | 8100 | 0.0 | - | +| 1.3007 | 8150 | 0.0001 | - | +| 1.3086 | 8200 | 0.0 | - | +| 1.3166 | 8250 | 0.0001 | - | +| 1.3246 | 8300 | 0.0 | - | +| 1.3326 | 8350 | 0.0 | - | +| 1.3406 | 8400 | 0.0 | - | +| 1.3485 | 8450 | 0.0 | - | +| 1.3565 | 8500 | 0.0 | - | +| 1.3645 | 8550 | 0.0 | - | +| 1.3725 | 8600 | 0.0 | - | +| 1.3805 | 8650 | 0.0 | - | +| 1.3884 | 8700 | 0.0 | - | +| 1.3964 | 8750 | 0.0 | - | +| 1.4044 | 8800 | 0.0 | - | +| 1.4124 | 8850 | 0.0 | - | +| 1.4204 | 8900 | 0.0 | - | +| 1.4283 | 8950 | 0.0 | - | +| 1.4363 | 9000 | 0.0 | - | +| 1.4443 | 9050 | 0.0 | - | +| 1.4523 | 9100 | 0.0 | - | +| 1.4603 | 9150 | 0.0 | - | +| 1.4682 | 9200 | 0.0 | - | +| 1.4762 | 9250 | 0.0 | - | +| 1.4842 | 9300 | 0.0242 | - | +| 1.4922 | 9350 | 0.0 | - | +| 1.5002 | 9400 | 0.0001 | - | +| 1.5081 | 9450 | 0.0 | - | +| 1.5161 | 9500 | 0.0 | - | +| 1.5241 | 9550 | 0.0 | - | +| 1.5321 | 9600 | 0.0 | - | +| 1.5401 | 9650 | 0.0 | - | +| 1.5480 | 9700 | 0.0 | - | +| 1.5560 | 9750 | 0.0 | - | +| 1.5640 | 9800 | 0.0 | - | +| 1.5720 | 9850 | 0.0 | - | +| 1.5800 | 9900 | 0.0 | - | +| 1.5879 | 9950 | 0.0 | - | +| 1.5959 | 10000 | 0.0 | - | +| 1.6039 | 10050 | 0.0 | - | +| 1.6119 | 10100 | 0.0 | - | +| 1.6199 | 10150 | 0.0 | - | +| 1.6278 | 10200 | 0.0002 | - | +| 1.6358 | 10250 | 0.0001 | - | +| 1.6438 | 10300 | 0.0 | - | +| 1.6518 | 10350 | 0.0 | - | +| 1.6598 | 10400 | 0.0 | - | +| 1.6677 | 10450 | 0.0 | - | +| 1.6757 | 10500 | 0.0 | - | +| 1.6837 | 10550 | 0.0 | - | +| 1.6917 | 10600 | 0.0 | - | +| 1.6996 | 10650 | 0.0 | - | +| 1.7076 | 10700 | 0.0 | - | +| 1.7156 | 10750 | 0.0 | - | +| 1.7236 | 10800 | 0.0 | - | +| 1.7316 | 10850 | 0.0 | - | +| 1.7395 | 10900 | 0.0 | - | +| 1.7475 | 10950 | 0.0 | - 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| +| **1.0** | **6266** | **-** | **0.2299** | +| 1.0054 | 6300 | 0.0 | - | +| 1.0134 | 6350 | 0.0 | - | +| 1.0214 | 6400 | 0.0 | - | +| 1.0294 | 6450 | 0.0 | - | +| 1.0373 | 6500 | 0.0 | - | +| 1.0453 | 6550 | 0.0 | - | +| 1.0533 | 6600 | 0.0 | - | +| 1.0613 | 6650 | 0.0 | - | +| 1.0693 | 6700 | 0.0 | - | +| 1.0772 | 6750 | 0.0 | - | +| 1.0852 | 6800 | 0.0 | - | +| 1.0932 | 6850 | 0.0 | - | +| 1.1012 | 6900 | 0.0 | - | +| 1.1092 | 6950 | 0.0 | - | +| 1.1171 | 7000 | 0.0 | - | +| 1.1251 | 7050 | 0.0 | - | +| 1.1331 | 7100 | 0.0604 | - | +| 1.1411 | 7150 | 0.0007 | - | +| 1.1491 | 7200 | 0.0002 | - | +| 1.1570 | 7250 | 0.0 | - | +| 1.1650 | 7300 | 0.0 | - | +| 1.1730 | 7350 | 0.0 | - | +| 1.1810 | 7400 | 0.0 | - | +| 1.1890 | 7450 | 0.0 | - | +| 1.1969 | 7500 | 0.0395 | - | +| 1.2049 | 7550 | 0.0 | - | +| 1.2129 | 7600 | 0.0 | - | +| 1.2209 | 7650 | 0.0 | - | +| 1.2289 | 7700 | 0.0 | - | +| 1.2368 | 7750 | 0.0 | - | +| 1.2448 | 7800 | 0.0 | - | +| 1.2528 | 7850 | 0.0 | - | +| 1.2608 | 7900 | 0.0 | - 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| +| 1.5480 | 9700 | 0.0 | - | +| 1.5560 | 9750 | 0.0 | - | +| 1.5640 | 9800 | 0.0 | - | +| 1.5720 | 9850 | 0.0 | - | +| 1.5800 | 9900 | 0.0 | - | +| 1.5879 | 9950 | 0.0 | - | +| 1.5959 | 10000 | 0.0 | - | +| 1.6039 | 10050 | 0.0 | - | +| 1.6119 | 10100 | 0.0 | - | +| 1.6199 | 10150 | 0.0 | - | +| 1.6278 | 10200 | 0.0001 | - | +| 1.6358 | 10250 | 0.0 | - | +| 1.6438 | 10300 | 0.0 | - | +| 1.6518 | 10350 | 0.0 | - | +| 1.6598 | 10400 | 0.0 | - | +| 1.6677 | 10450 | 0.0 | - | +| 1.6757 | 10500 | 0.0 | - | +| 1.6837 | 10550 | 0.0 | - | +| 1.6917 | 10600 | 0.0 | - | +| 1.6996 | 10650 | 0.0 | - | +| 1.7076 | 10700 | 0.0 | - | +| 1.7156 | 10750 | 0.0 | - | +| 1.7236 | 10800 | 0.0 | - | +| 1.7316 | 10850 | 0.0 | - | +| 1.7395 | 10900 | 0.0 | - | +| 1.7475 | 10950 | 0.0 | - | +| 1.7555 | 11000 | 0.0 | - | +| 1.7635 | 11050 | 0.0 | - | +| 1.7715 | 11100 | 0.0 | - | +| 1.7794 | 11150 | 0.0 | - | +| 1.7874 | 11200 | 0.0 | - | +| 1.7954 | 11250 | 0.0289 | - | +| 1.8034 | 11300 | 0.0 | - | +| 1.8114 | 11350 | 0.0 | - | +| 1.8193 | 11400 | 0.0 | - | +| 1.8273 | 11450 | 0.0 | - | +| 1.8353 | 11500 | 0.0 | - | +| 1.8433 | 11550 | 0.0 | - | +| 1.8513 | 11600 | 0.0 | - | +| 1.8592 | 11650 | 0.0 | - | +| 1.8672 | 11700 | 0.0 | - | +| 1.8752 | 11750 | 0.0 | - | +| 1.8832 | 11800 | 0.0 | - | +| 1.8912 | 11850 | 0.0 | - | +| 1.8991 | 11900 | 0.0 | - | +| 1.9071 | 11950 | 0.0 | - | +| 1.9151 | 12000 | 0.0 | - | +| 1.9231 | 12050 | 0.0 | - | +| 1.9311 | 12100 | 0.0 | - | +| 1.9390 | 12150 | 0.0 | - | +| 1.9470 | 12200 | 0.0 | - | +| 1.9550 | 12250 | 0.0 | - | +| 1.9630 | 12300 | 0.0 | - | +| 1.9710 | 12350 | 0.0 | - | +| 1.9789 | 12400 | 0.0 | - | +| 1.9869 | 12450 | 0.0 | - | +| 1.9949 | 12500 | 0.0 | - | +| 2.0 | 12532 | - | 0.2718 | +| 2.0029 | 12550 | 0.0 | - | +| 2.0109 | 12600 | 0.0 | - | +| 2.0188 | 12650 | 0.0 | - | +| 2.0268 | 12700 | 0.0 | - | +| 2.0348 | 12750 | 0.0 | - | +| 2.0428 | 12800 | 0.0 | - | +| 2.0508 | 12850 | 0.0 | - | +| 2.0587 | 12900 | 0.0 | - | +| 2.0667 | 12950 | 0.0 | - | +| 2.0747 | 13000 | 0.0 | - | +| 2.0827 | 13050 | 0.0 | - | +| 2.0906 | 13100 | 0.0 | - | +| 2.0986 | 13150 | 0.0 | - | +| 2.1066 | 13200 | 0.0 | - | +| 2.1146 | 13250 | 0.0 | - | +| 2.1226 | 13300 | 0.0 | - | +| 2.1305 | 13350 | 0.0 | - | +| 2.1385 | 13400 | 0.0 | - | +| 2.1465 | 13450 | 0.0 | - | +| 2.1545 | 13500 | 0.0 | - | +| 2.1625 | 13550 | 0.0037 | - | +| 2.1704 | 13600 | 0.0 | - | +| 2.1784 | 13650 | 0.0 | - | +| 2.1864 | 13700 | 0.0 | - | +| 2.1944 | 13750 | 0.0 | - | +| 2.2024 | 13800 | 0.0 | - | +| 2.2103 | 13850 | 0.0 | - | +| 2.2183 | 13900 | 0.0 | - | +| 2.2263 | 13950 | 0.0 | - | +| 2.2343 | 14000 | 0.0 | - | +| 2.2423 | 14050 | 0.0 | - | +| 2.2502 | 14100 | 0.0 | - | +| 2.2582 | 14150 | 0.0 | - | +| 2.2662 | 14200 | 0.0 | - | +| 2.2742 | 14250 | 0.0 | - | +| 2.2822 | 14300 | 0.0 | - | +| 2.2901 | 14350 | 0.0009 | - | +| 2.2981 | 14400 | 0.0 | - | +| 2.3061 | 14450 | 0.0 | - | +| 2.3141 | 14500 | 0.0 | - | +| 2.3221 | 14550 | 0.0 | - | +| 2.3300 | 14600 | 0.0 | - | +| 2.3380 | 14650 | 0.0028 | - | +| 2.3460 | 14700 | 0.0 | - | +| 2.3540 | 14750 | 0.0 | - | +| 2.3620 | 14800 | 0.0 | - | +| 2.3699 | 14850 | 0.0 | - | +| 2.3779 | 14900 | 0.0 | - | +| 2.3859 | 14950 | 0.0 | - | +| 2.3939 | 15000 | 0.0 | - | +| 2.4019 | 15050 | 0.0 | - | +| 2.4098 | 15100 | 0.0 | - | +| 2.4178 | 15150 | 0.0 | - | +| 2.4258 | 15200 | 0.0 | - | +| 2.4338 | 15250 | 0.0022 | - | +| 2.4417 | 15300 | 0.0 | - | +| 2.4497 | 15350 | 0.0 | - | +| 2.4577 | 15400 | 0.0 | - | +| 2.4657 | 15450 | 0.0 | - | +| 2.4737 | 15500 | 0.0 | - | +| 2.4816 | 15550 | 0.0 | - | +| 2.4896 | 15600 | 0.0 | - | +| 2.4976 | 15650 | 0.0 | - | +| 2.5056 | 15700 | 0.0 | - | +| 2.5136 | 15750 | 0.0 | - | +| 2.5215 | 15800 | 0.0001 | - | +| 2.5295 | 15850 | 0.0 | - | +| 2.5375 | 15900 | 0.0 | - | +| 2.5455 | 15950 | 0.0 | - | +| 2.5535 | 16000 | 0.0 | - | +| 2.5614 | 16050 | 0.0 | - | +| 2.5694 | 16100 | 0.0 | - | +| 2.5774 | 16150 | 0.0 | - | +| 2.5854 | 16200 | 0.0 | - | +| 2.5934 | 16250 | 0.0 | - | +| 2.6013 | 16300 | 0.0 | - | +| 2.6093 | 16350 | 0.0 | - | +| 2.6173 | 16400 | 0.0 | - | +| 2.6253 | 16450 | 0.0 | - | +| 2.6333 | 16500 | 0.0 | - | +| 2.6412 | 16550 | 0.0 | - | +| 2.6492 | 16600 | 0.0 | - | +| 2.6572 | 16650 | 0.0 | - | +| 2.6652 | 16700 | 0.0 | - | +| 2.6732 | 16750 | 0.0 | - | +| 2.6811 | 16800 | 0.0 | - | +| 2.6891 | 16850 | 0.0 | - | +| 2.6971 | 16900 | 0.0 | - | +| 2.7051 | 16950 | 0.0 | - | +| 2.7131 | 17000 | 0.0 | - | +| 2.7210 | 17050 | 0.0 | - | +| 2.7290 | 17100 | 0.0 | - | +| 2.7370 | 17150 | 0.0 | - | +| 2.7450 | 17200 | 0.0 | - | +| 2.7530 | 17250 | 0.0 | - | +| 2.7609 | 17300 | 0.0 | - | +| 2.7689 | 17350 | 0.0 | - | +| 2.7769 | 17400 | 0.0 | - | +| 2.7849 | 17450 | 0.0 | - | +| 2.7929 | 17500 | 0.0 | - | +| 2.8008 | 17550 | 0.0 | - | +| 2.8088 | 17600 | 0.0 | - | +| 2.8168 | 17650 | 0.0 | - | +| 2.8248 | 17700 | 0.0 | - | +| 2.8327 | 17750 | 0.0 | - | +| 2.8407 | 17800 | 0.0 | - | +| 2.8487 | 17850 | 0.0 | - | +| 2.8567 | 17900 | 0.0 | - | +| 2.8647 | 17950 | 0.0 | - | +| 2.8726 | 18000 | 0.0624 | - | +| 2.8806 | 18050 | 0.0 | - | +| 2.8886 | 18100 | 0.0 | - | +| 2.8966 | 18150 | 0.0 | - | +| 2.9046 | 18200 | 0.0 | - | +| 2.9125 | 18250 | 0.0 | - | +| 2.9205 | 18300 | 0.0 | - | +| 2.9285 | 18350 | 0.0 | - | +| 2.9365 | 18400 | 0.0 | - | +| 2.9445 | 18450 | 0.0 | - | +| 2.9524 | 18500 | 0.0 | - | +| 2.9604 | 18550 | 0.0 | - | +| 2.9684 | 18600 | 0.0 | - | +| 2.9764 | 18650 | 0.0 | - | +| 2.9844 | 18700 | 0.0 | - | +| 2.9923 | 18750 | 0.0 | - | +| 3.0 | 18798 | - | 0.2642 | +| 3.0003 | 18800 | 0.0 | - | +| 3.0083 | 18850 | 0.0 | - | +| 3.0163 | 18900 | 0.0 | - | +| 3.0243 | 18950 | 0.0 | - | +| 3.0322 | 19000 | 0.0 | - | +| 3.0402 | 19050 | 0.0 | - | +| 3.0482 | 19100 | 0.0 | - | +| 3.0562 | 19150 | 0.0 | - | +| 3.0642 | 19200 | 0.0 | - | +| 3.0721 | 19250 | 0.0 | - | +| 3.0801 | 19300 | 0.0 | - | +| 3.0881 | 19350 | 0.0 | - | +| 3.0961 | 19400 | 0.0 | - | +| 3.1041 | 19450 | 0.0 | - | +| 3.1120 | 19500 | 0.0 | - | +| 3.1200 | 19550 | 0.0 | - | +| 3.1280 | 19600 | 0.0 | - | +| 3.1360 | 19650 | 0.0 | - | +| 3.1440 | 19700 | 0.0 | - | +| 3.1519 | 19750 | 0.0 | - | +| 3.1599 | 19800 | 0.0 | - | +| 3.1679 | 19850 | 0.0 | - | +| 3.1759 | 19900 | 0.0 | - | +| 3.1838 | 19950 | 0.0 | - | +| 3.1918 | 20000 | 0.0 | - | +| 3.1998 | 20050 | 0.0 | - | +| 3.2078 | 20100 | 0.0 | - | +| 3.2158 | 20150 | 0.0 | - | +| 3.2237 | 20200 | 0.0 | - | +| 3.2317 | 20250 | 0.0 | - | +| 3.2397 | 20300 | 0.0418 | - | +| 3.2477 | 20350 | 0.0 | - | +| 3.2557 | 20400 | 0.0 | - | +| 3.2636 | 20450 | 0.0 | - | +| 3.2716 | 20500 | 0.0 | - | +| 3.2796 | 20550 | 0.0077 | - | +| 3.2876 | 20600 | 0.0 | - | +| 3.2956 | 20650 | 0.0 | - | +| 3.3035 | 20700 | 0.0 | - | +| 3.3115 | 20750 | 0.0 | - | +| 3.3195 | 20800 | 0.0 | - | +| 3.3275 | 20850 | 0.0 | - | +| 3.3355 | 20900 | 0.0 | - | +| 3.3434 | 20950 | 0.0 | - | +| 3.3514 | 21000 | 0.0 | - | +| 3.3594 | 21050 | 0.0 | - | +| 3.3674 | 21100 | 0.0 | - | +| 3.3754 | 21150 | 0.0 | - | +| 3.3833 | 21200 | 0.0 | - | +| 3.3913 | 21250 | 0.0 | - | +| 3.3993 | 21300 | 0.0 | - | +| 3.4073 | 21350 | 0.0 | - | +| 3.4153 | 21400 | 0.0 | - | +| 3.4232 | 21450 | 0.0 | - | +| 3.4312 | 21500 | 0.0 | - | +| 3.4392 | 21550 | 0.0 | - | +| 3.4472 | 21600 | 0.0 | - | +| 3.4552 | 21650 | 0.0 | - | +| 3.4631 | 21700 | 0.0 | - | +| 3.4711 | 21750 | 0.0 | - | +| 3.4791 | 21800 | 0.0 | - | +| 3.4871 | 21850 | 0.0 | - | +| 3.4951 | 21900 | 0.0 | - | +| 3.5030 | 21950 | 0.0 | - | +| 3.5110 | 22000 | 0.0 | - | +| 3.5190 | 22050 | 0.0 | - | +| 3.5270 | 22100 | 0.0 | - | +| 3.5350 | 22150 | 0.0 | - | +| 3.5429 | 22200 | 0.0 | - | +| 3.5509 | 22250 | 0.0 | - | +| 3.5589 | 22300 | 0.0 | - | +| 3.5669 | 22350 | 0.0 | - | +| 3.5748 | 22400 | 0.0 | - | +| 3.5828 | 22450 | 0.0 | - | +| 3.5908 | 22500 | 0.0 | - | +| 3.5988 | 22550 | 0.0 | - | +| 3.6068 | 22600 | 0.0 | - | +| 3.6147 | 22650 | 0.0 | - | +| 3.6227 | 22700 | 0.0 | - | +| 3.6307 | 22750 | 0.0 | - | +| 3.6387 | 22800 | 0.0 | - | +| 3.6467 | 22850 | 0.0 | - | +| 3.6546 | 22900 | 0.0 | - | +| 3.6626 | 22950 | 0.0 | - | +| 3.6706 | 23000 | 0.0 | - | +| 3.6786 | 23050 | 0.0 | - | +| 3.6866 | 23100 | 0.0 | - | +| 3.6945 | 23150 | 0.0 | - | +| 3.7025 | 23200 | 0.0 | - | +| 3.7105 | 23250 | 0.0 | - | +| 3.7185 | 23300 | 0.0 | - | +| 3.7265 | 23350 | 0.0 | - | +| 3.7344 | 23400 | 0.0 | - | +| 3.7424 | 23450 | 0.0 | - | +| 3.7504 | 23500 | 0.0 | - | +| 3.7584 | 23550 | 0.0 | - | +| 3.7664 | 23600 | 0.0 | - | +| 3.7743 | 23650 | 0.0 | - | +| 3.7823 | 23700 | 0.0 | - | +| 3.7903 | 23750 | 0.0 | - | +| 3.7983 | 23800 | 0.0 | - | +| 3.8063 | 23850 | 0.0 | - | +| 3.8142 | 23900 | 0.0 | - | +| 3.8222 | 23950 | 0.0 | - | +| 3.8302 | 24000 | 0.0 | - | +| 3.8382 | 24050 | 0.0 | - | +| 3.8462 | 24100 | 0.0 | - | +| 3.8541 | 24150 | 0.0 | - | +| 3.8621 | 24200 | 0.0 | - | +| 3.8701 | 24250 | 0.0 | - | +| 3.8781 | 24300 | 0.0 | - | +| 3.8861 | 24350 | 0.0 | - | +| 3.8940 | 24400 | 0.0 | - | +| 3.9020 | 24450 | 0.0 | - | +| 3.9100 | 24500 | 0.0 | - | +| 3.9180 | 24550 | 0.0 | - | +| 3.9259 | 24600 | 0.0 | - | +| 3.9339 | 24650 | 0.0 | - | +| 3.9419 | 24700 | 0.0 | - | +| 3.9499 | 24750 | 0.0 | - | +| 3.9579 | 24800 | 0.0 | - | +| 3.9658 | 24850 | 0.0 | - | +| 3.9738 | 24900 | 0.0 | - | +| 3.9818 | 24950 | 0.0 | - | +| 3.9898 | 25000 | 0.0 | - | +| 3.9978 | 25050 | 0.0 | - | +| 4.0 | 25064 | - | 0.2557 | + +* The bold row denotes the saved checkpoint. +### Framework Versions +- Python: 3.10.12 +- SetFit: 1.0.3 +- Sentence Transformers: 2.7.0 +- Transformers: 4.40.2 +- PyTorch: 2.2.1+cu121 +- Datasets: 2.19.1 +- Tokenizers: 0.19.1 + +## Citation + +### BibTeX +```bibtex +@article{https://doi.org/10.48550/arxiv.2209.11055, + doi = {10.48550/ARXIV.2209.11055}, + url = {https://arxiv.org/abs/2209.11055}, + author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, + keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, + title = {Efficient Few-Shot Learning Without Prompts}, + publisher = {arXiv}, + year = {2022}, + copyright = {Creative Commons Attribution 4.0 International} +} +``` + + + + + + \ No newline at end of file