metadata
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: ' SÃ, pues pedirle a MarÃa 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: "Â\_A ver, pon la carta de Elba Esther. Es que luego la borró. Fue en mayo del 23, 2 de mayo: ‘Ahà le espero con el Ejército —supuestamente esto es lo que le dijo Calderón a la maestra Elba Esther, ahà 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 oponÃamos 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 tomarÃamos las instalaciones del Issste y justo esa fue su respuesta: Ahà 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.’ Aquà me está contestando a mÃ, porque yo dije que a ella le habÃan entregado por ayudar en el fraude, que no me dirÃa la maestra que no ayudó en el fraude del 2006, y a cambio yo sostengo que le entregaron el Issste, la SubsecretarÃa de Educación Pública y la LoterÃa 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 ilÃcito 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 paÃs 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 importantÃsimo 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 paÃs y por eso son muy buenas estas mañaneras. Pero, bueno, eso es lo que querÃamos decir. ¿Qué se está haciendo? Procurar, ya es un compromiso, garantizar el derecho a la salud. Y vaya que ha costado, por estos intereses. ImagÃnense, no se podÃan comprar medicinas en el extranjero porque la ley lo prohibÃa, lo impedÃa; 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 minorÃas, no representantes del pueblo, esa es nuestra diferencia de fondo. No es nada personal, pero sà es importante el darle su sitio que le corresponde a lo público. República es,Â\_res publica, cosa pública. Si vivimos en una república, tenemos que pensar en eso, en lo público. Eso ya se habÃa 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 especÃfico 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 model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- 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
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 128 tokens
- Number of Classes: 4 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
Label | Examples |
---|---|
0 |
|
1 |
|
2 |
|
3 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.7890 |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
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 | - |
1.7555 | 11000 | 0.0 | - |
1.7635 | 11050 | 0.0 | - |
1.7715 | 11100 | 0.0 | - |
1.7794 | 11150 | 0.0 | - |
1.7874 | 11200 | 0.0002 | - |
1.7954 | 11250 | 0.0228 | - |
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 | - | 0.2568 |
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 | 0.005 | - |
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 | 0.0005 | - |
2.2981 | 14400 | 0.0 | - |
2.3061 | 14450 | 0.0001 | - |
2.3141 | 14500 | 0.0 | - |
2.3221 | 14550 | 0.0 | - |
2.3300 | 14600 | 0.0 | - |
2.3380 | 14650 | 0.0012 | - |
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 | 0.0017 | - |
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 | 0.0002 | - |
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 | 0.0001 | - |
2.8407 | 17800 | 0.0 | - |
2.8487 | 17850 | 0.0 | - |
2.8567 | 17900 | 0.0 | - |
2.8647 | 17950 | 0.0 | - |
2.8726 | 18000 | 0.0623 | - |
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 | - | 0.2418 |
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 | 0.0448 | - |
3.2477 | 20350 | 0.0 | - |
3.2557 | 20400 | 0.0 | - |
3.2636 | 20450 | 0.0 | - |
3.2716 | 20500 | 0.0001 | - |
3.2796 | 20550 | 0.0102 | - |
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 | - | 0.2438 |
0.0002 | 1 | 0.0 | - |
0.0080 | 50 | 0.0 | - |
0.0160 | 100 | 0.0 | - |
0.0239 | 150 | 0.0 | - |
0.0319 | 200 | 0.0 | - |
0.0399 | 250 | 0.0 | - |
0.0479 | 300 | 0.0 | - |
0.0559 | 350 | 0.0 | - |
0.0638 | 400 | 0.0 | - |
0.0718 | 450 | 0.0 | - |
0.0798 | 500 | 0.0 | - |
0.0878 | 550 | 0.0 | - |
0.0958 | 600 | 0.0 | - |
0.1037 | 650 | 0.0 | - |
0.1117 | 700 | 0.0 | - |
0.1197 | 750 | 0.0 | - |
0.1277 | 800 | 0.0 | - |
0.1357 | 850 | 0.0 | - |
0.1436 | 900 | 0.0 | - |
0.1516 | 950 | 0.0 | - |
0.1596 | 1000 | 0.0 | - |
0.1676 | 1050 | 0.0 | - |
0.1756 | 1100 | 0.0 | - |
0.1835 | 1150 | 0.0 | - |
0.1915 | 1200 | 0.0 | - |
0.1995 | 1250 | 0.0 | - |
0.2075 | 1300 | 0.0 | - |
0.2154 | 1350 | 0.0 | - |
0.2234 | 1400 | 0.0 | - |
0.2314 | 1450 | 0.0019 | - |
0.2394 | 1500 | 0.0 | - |
0.2474 | 1550 | 0.0 | - |
0.2553 | 1600 | 0.0 | - |
0.2633 | 1650 | 0.0 | - |
0.2713 | 1700 | 0.0 | - |
0.2793 | 1750 | 0.0 | - |
0.2873 | 1800 | 0.0 | - |
0.2952 | 1850 | 0.0 | - |
0.3032 | 1900 | 0.0 | - |
0.3112 | 1950 | 0.0 | - |
0.3192 | 2000 | 0.0 | - |
0.3272 | 2050 | 0.0 | - |
0.3351 | 2100 | 0.0 | - |
0.3431 | 2150 | 0.0001 | - |
0.3511 | 2200 | 0.0319 | - |
0.3591 | 2250 | 0.0 | - |
0.3671 | 2300 | 0.0 | - |
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 | 0.0 | - |
0.4229 | 2650 | 0.0 | - |
0.4309 | 2700 | 0.0 | - |
0.4389 | 2750 | 0.0001 | - |
0.4469 | 2800 | 0.0 | - |
0.4548 | 2850 | 0.0 | - |
0.4628 | 2900 | 0.0 | - |
0.4708 | 2950 | 0.0 | - |
0.4788 | 3000 | 0.0 | - |
0.4868 | 3050 | 0.0 | - |
0.4947 | 3100 | 0.0 | - |
0.5027 | 3150 | 0.0 | - |
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 | 0.0 | - |
0.5586 | 3500 | 0.0 | - |
0.5665 | 3550 | 0.0003 | - |
0.5745 | 3600 | 0.0 | - |
0.5825 | 3650 | 0.0 | - |
0.5905 | 3700 | 0.0 | - |
0.5985 | 3750 | 0.0 | - |
0.6064 | 3800 | 0.0 | - |
0.6144 | 3850 | 0.0 | - |
0.6224 | 3900 | 0.0 | - |
0.6304 | 3950 | 0.0 | - |
0.6384 | 4000 | 0.0 | - |
0.6463 | 4050 | 0.0 | - |
0.6543 | 4100 | 0.0 | - |
0.6623 | 4150 | 0.0 | - |
0.6703 | 4200 | 0.0 | - |
0.6783 | 4250 | 0.0 | - |
0.6862 | 4300 | 0.0 | - |
0.6942 | 4350 | 0.0 | - |
0.7022 | 4400 | 0.0 | - |
0.7102 | 4450 | 0.0 | - |
0.7182 | 4500 | 0.0 | - |
0.7261 | 4550 | 0.0 | - |
0.7341 | 4600 | 0.0 | - |
0.7421 | 4650 | 0.0 | - |
0.7501 | 4700 | 0.0 | - |
0.7581 | 4750 | 0.0 | - |
0.7660 | 4800 | 0.0 | - |
0.7740 | 4850 | 0.0602 | - |
0.7820 | 4900 | 0.0 | - |
0.7900 | 4950 | 0.0 | - |
0.7980 | 5000 | 0.0 | - |
0.8059 | 5050 | 0.0 | - |
0.8139 | 5100 | 0.0002 | - |
0.8219 | 5150 | 0.0 | - |
0.8299 | 5200 | 0.0001 | - |
0.8379 | 5250 | 0.0 | - |
0.8458 | 5300 | 0.0 | - |
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 | 0.0 | - |
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 | 0.0 | - |
0.9895 | 6200 | 0.0 | - |
0.9974 | 6250 | 0.0 | - |
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 | - |
1.2688 | 7950 | 0.0 | - |
1.2767 | 8000 | 0.0 | - |
1.2847 | 8050 | 0.0 | - |
1.2927 | 8100 | 0.0 | - |
1.3007 | 8150 | 0.0002 | - |
1.3086 | 8200 | 0.0 | - |
1.3166 | 8250 | 0.0 | - |
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 | 0.0093 | - |
1.4922 | 9350 | 0.0 | - |
1.5002 | 9400 | 0.0 | - |
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 | 0.0001 | - |
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
@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}
}