metadata
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
Suasana:Tempatnya ramai sekali dan ngantei banget. Suasana di dalam resto
sangat panas dan padat. Makanannya enak enak.
- text: >-
bener2 pedes puolll:Rasanya sgt gak cocok dilidah gue orang bekasi..
ayamnya ayam kampung sih tp kecil bgt (beli yg dada).. terus tempe bacem
sgt padet dan tahunya enak sih.. untuk sambel pedes bgt bener2 pedes
puolll, tp rasanya gasukaa.
- text: >-
gang:Suasana di dalam resto sangat panas dan padat. Makanannya enak enak.
Dan restonya ada di beberapa tempat dalam satu gang.
- text: >-
tempe:Menu makanannya khas Sunda ada ayam, pepes ikan, babat, tahu, tempe,
sayur-sayur. Tidak banyak variasinya tapi kualitas rasanya oke. Saat itu
pesen ayam bakar, jukut goreng, tempe sama pepes tahu. Ini semuanya enak
(menurut pendapat pribadi).
- text: >-
babat:Kemaren kebetulan makan babat sama nyobain cumi, buat tekstur
babatnya itu engga alot sama sekali dan tidak amis, sedangkan buat cumi
utuh lumayan gede juga tekstur kenyel kenyelnya dapet dan mateng juga
sampe ke dalem.
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit Aspect Model
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.80625
name: Accuracy
SetFit Aspect Model
This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). A LogisticRegression instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.
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.
This model was trained within the context of a larger system for ABSA, which looks like so:
- Use a spaCy model to select possible aspect span candidates.
- Use this SetFit model to filter these possible aspect span candidates.
- Use a SetFit model to classify the filtered aspect span candidates.
Model Details
Model Description
- Model Type: SetFit
- Classification head: a LogisticRegression instance
- spaCy Model: id_core_news_trf
- SetFitABSA Aspect Model: pahri/setfit-indo-resto-RM-ibu-imas-aspect
- SetFitABSA Polarity Model: pahri/setfit-indo-resto-RM-ibu-imas-polarity
- Maximum Sequence Length: 512 tokens
- Number of Classes: 2 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 |
---|---|
no aspect |
|
aspect |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.8063 |
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 AbsaModel
# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
"pahri/setfit-indo-resto-RM-ibu-imas-aspect",
"pahri/setfit-indo-resto-RM-ibu-imas-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 4 | 37.7180 | 93 |
Label | Training Sample Count |
---|---|
no aspect | 371 |
aspect | 51 |
Training Hyperparameters
- batch_size: (6, 6)
- num_epochs: (1, 16)
- 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: True
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0000 | 1 | 0.4225 | - |
0.0021 | 50 | 0.2528 | - |
0.0043 | 100 | 0.3611 | - |
0.0064 | 150 | 0.2989 | - |
0.0085 | 200 | 0.2907 | - |
0.0107 | 250 | 0.1609 | - |
0.0128 | 300 | 0.3534 | - |
0.0149 | 350 | 0.1294 | - |
0.0171 | 400 | 0.2797 | - |
0.0192 | 450 | 0.3119 | - |
0.0213 | 500 | 0.004 | - |
0.0235 | 550 | 0.1057 | - |
0.0256 | 600 | 0.1049 | - |
0.0277 | 650 | 0.1601 | - |
0.0299 | 700 | 0.151 | - |
0.0320 | 750 | 0.1034 | - |
0.0341 | 800 | 0.2356 | - |
0.0363 | 850 | 0.1335 | - |
0.0384 | 900 | 0.0559 | - |
0.0405 | 950 | 0.0028 | - |
0.0427 | 1000 | 0.1307 | - |
0.0448 | 1050 | 0.0049 | - |
0.0469 | 1100 | 0.1348 | - |
0.0491 | 1150 | 0.0392 | - |
0.0512 | 1200 | 0.054 | - |
0.0533 | 1250 | 0.0016 | - |
0.0555 | 1300 | 0.0012 | - |
0.0576 | 1350 | 0.0414 | - |
0.0597 | 1400 | 0.1087 | - |
0.0618 | 1450 | 0.0464 | - |
0.0640 | 1500 | 0.0095 | - |
0.0661 | 1550 | 0.0011 | - |
0.0682 | 1600 | 0.0002 | - |
0.0704 | 1650 | 0.1047 | - |
0.0725 | 1700 | 0.001 | - |
0.0746 | 1750 | 0.0965 | - |
0.0768 | 1800 | 0.0002 | - |
0.0789 | 1850 | 0.1436 | - |
0.0810 | 1900 | 0.0011 | - |
0.0832 | 1950 | 0.001 | - |
0.0853 | 2000 | 0.1765 | - |
0.0874 | 2050 | 0.1401 | - |
0.0896 | 2100 | 0.0199 | - |
0.0917 | 2150 | 0.0 | - |
0.0938 | 2200 | 0.0023 | - |
0.0960 | 2250 | 0.0034 | - |
0.0981 | 2300 | 0.0001 | - |
0.1002 | 2350 | 0.0948 | - |
0.1024 | 2400 | 0.1634 | - |
0.1045 | 2450 | 0.0 | - |
0.1066 | 2500 | 0.0005 | - |
0.1088 | 2550 | 0.0695 | - |
0.1109 | 2600 | 0.0 | - |
0.1130 | 2650 | 0.0067 | - |
0.1152 | 2700 | 0.0025 | - |
0.1173 | 2750 | 0.0013 | - |
0.1194 | 2800 | 0.1426 | - |
0.1216 | 2850 | 0.0001 | - |
0.1237 | 2900 | 0.0 | - |
0.1258 | 2950 | 0.0 | - |
0.1280 | 3000 | 0.0001 | - |
0.1301 | 3050 | 0.0001 | - |
0.1322 | 3100 | 0.0122 | - |
0.1344 | 3150 | 0.0 | - |
0.1365 | 3200 | 0.0001 | - |
0.1386 | 3250 | 0.0041 | - |
0.1408 | 3300 | 0.2549 | - |
0.1429 | 3350 | 0.0062 | - |
0.1450 | 3400 | 0.0154 | - |
0.1472 | 3450 | 0.1776 | - |
0.1493 | 3500 | 0.0039 | - |
0.1514 | 3550 | 0.0183 | - |
0.1536 | 3600 | 0.0045 | - |
0.1557 | 3650 | 0.1108 | - |
0.1578 | 3700 | 0.0002 | - |
0.1600 | 3750 | 0.01 | - |
0.1621 | 3800 | 0.0002 | - |
0.1642 | 3850 | 0.0001 | - |
0.1664 | 3900 | 0.1612 | - |
0.1685 | 3950 | 0.0107 | - |
0.1706 | 4000 | 0.0548 | - |
0.1728 | 4050 | 0.0001 | - |
0.1749 | 4100 | 0.0162 | - |
0.1770 | 4150 | 0.1294 | - |
0.1792 | 4200 | 0.0 | - |
0.1813 | 4250 | 0.0032 | - |
0.1834 | 4300 | 0.0051 | - |
0.1855 | 4350 | 0.0 | - |
0.1877 | 4400 | 0.0151 | - |
0.1898 | 4450 | 0.0097 | - |
0.1919 | 4500 | 0.0002 | - |
0.1941 | 4550 | 0.0045 | - |
0.1962 | 4600 | 0.0001 | - |
0.1983 | 4650 | 0.0001 | - |
0.2005 | 4700 | 0.0227 | - |
0.2026 | 4750 | 0.0018 | - |
0.2047 | 4800 | 0.0 | - |
0.2069 | 4850 | 0.0001 | - |
0.2090 | 4900 | 0.0 | - |
0.2111 | 4950 | 0.0 | - |
0.2133 | 5000 | 0.0 | - |
0.2154 | 5050 | 0.0002 | - |
0.2175 | 5100 | 0.0002 | - |
0.2197 | 5150 | 0.0038 | - |
0.2218 | 5200 | 0.0 | - |
0.2239 | 5250 | 0.0 | - |
0.2261 | 5300 | 0.0 | - |
0.2282 | 5350 | 0.0028 | - |
0.2303 | 5400 | 0.0 | - |
0.2325 | 5450 | 0.1146 | - |
0.2346 | 5500 | 0.0 | - |
0.2367 | 5550 | 0.0073 | - |
0.2389 | 5600 | 0.0467 | - |
0.2410 | 5650 | 0.0092 | - |
0.2431 | 5700 | 0.0196 | - |
0.2453 | 5750 | 0.0002 | - |
0.2474 | 5800 | 0.0043 | - |
0.2495 | 5850 | 0.0378 | - |
0.2517 | 5900 | 0.0049 | - |
0.2538 | 5950 | 0.0054 | - |
0.2559 | 6000 | 0.1757 | - |
0.2581 | 6050 | 0.0 | - |
0.2602 | 6100 | 0.0001 | - |
0.2623 | 6150 | 0.1327 | - |
0.2645 | 6200 | 0.0 | - |
0.2666 | 6250 | 0.0 | - |
0.2687 | 6300 | 0.0 | - |
0.2709 | 6350 | 0.0134 | - |
0.2730 | 6400 | 0.0001 | - |
0.2751 | 6450 | 0.0112 | - |
0.2773 | 6500 | 0.0864 | - |
0.2794 | 6550 | 0.0 | - |
0.2815 | 6600 | 0.0094 | - |
0.2837 | 6650 | 0.1358 | - |
0.2858 | 6700 | 0.0155 | - |
0.2879 | 6750 | 0.0025 | - |
0.2901 | 6800 | 0.0002 | - |
0.2922 | 6850 | 0.0001 | - |
0.2943 | 6900 | 0.2809 | - |
0.2965 | 6950 | 0.0 | - |
0.2986 | 7000 | 0.0242 | - |
0.3007 | 7050 | 0.0015 | - |
0.3028 | 7100 | 0.0 | - |
0.3050 | 7150 | 0.1064 | - |
0.3071 | 7200 | 0.1636 | - |
0.3092 | 7250 | 0.267 | - |
0.3114 | 7300 | 0.1656 | - |
0.3135 | 7350 | 0.0943 | - |
0.3156 | 7400 | 0.189 | - |
0.3178 | 7450 | 0.0055 | - |
0.3199 | 7500 | 0.1286 | - |
0.3220 | 7550 | 0.1062 | - |
0.3242 | 7600 | 0.1275 | - |
0.3263 | 7650 | 0.0101 | - |
0.3284 | 7700 | 0.0162 | - |
0.3306 | 7750 | 0.0001 | - |
0.3327 | 7800 | 0.0001 | - |
0.3348 | 7850 | 0.0003 | - |
0.3370 | 7900 | 0.0 | - |
0.3391 | 7950 | 0.135 | - |
0.3412 | 8000 | 0.0 | - |
0.3434 | 8050 | 0.0125 | - |
0.3455 | 8100 | 0.0004 | - |
0.3476 | 8150 | 0.0 | - |
0.3498 | 8200 | 0.2229 | - |
0.3519 | 8250 | 0.0 | - |
0.3540 | 8300 | 0.0051 | - |
0.3562 | 8350 | 0.0 | - |
0.3583 | 8400 | 0.0001 | - |
0.3604 | 8450 | 0.0 | - |
0.3626 | 8500 | 0.1261 | - |
0.3647 | 8550 | 0.0054 | - |
0.3668 | 8600 | 0.1636 | - |
0.3690 | 8650 | 0.0036 | - |
0.3711 | 8700 | 0.0 | - |
0.3732 | 8750 | 0.0027 | - |
0.3754 | 8800 | 0.0 | - |
0.3775 | 8850 | 0.1422 | - |
0.3796 | 8900 | 0.1314 | - |
0.3818 | 8950 | 0.003 | - |
0.3839 | 9000 | 0.0 | - |
0.3860 | 9050 | 0.0092 | - |
0.3882 | 9100 | 0.0129 | - |
0.3903 | 9150 | 0.0 | - |
0.3924 | 9200 | 0.0 | - |
0.3946 | 9250 | 0.1659 | - |
0.3967 | 9300 | 0.0 | - |
0.3988 | 9350 | 0.0 | - |
0.4010 | 9400 | 0.0085 | - |
0.4031 | 9450 | 0.0 | - |
0.4052 | 9500 | 0.0 | - |
0.4074 | 9550 | 0.0 | - |
0.4095 | 9600 | 0.0112 | - |
0.4116 | 9650 | 0.0 | - |
0.4138 | 9700 | 0.0154 | - |
0.4159 | 9750 | 0.0011 | - |
0.4180 | 9800 | 0.0077 | - |
0.4202 | 9850 | 0.0064 | - |
0.4223 | 9900 | 0.0 | - |
0.4244 | 9950 | 0.0 | - |
0.4265 | 10000 | 0.0121 | - |
0.4287 | 10050 | 0.0 | - |
0.4308 | 10100 | 0.0 | - |
0.4329 | 10150 | 0.0076 | - |
0.4351 | 10200 | 0.0039 | - |
0.4372 | 10250 | 0.2153 | - |
0.4393 | 10300 | 0.0 | - |
0.4415 | 10350 | 0.1218 | - |
0.4436 | 10400 | 0.0077 | - |
0.4457 | 10450 | 0.1311 | - |
0.4479 | 10500 | 0.0 | - |
0.4500 | 10550 | 0.0 | - |
0.4521 | 10600 | 0.0 | - |
0.4543 | 10650 | 0.0041 | - |
0.4564 | 10700 | 0.0073 | - |
0.4585 | 10750 | 0.0051 | - |
0.4607 | 10800 | 0.0 | - |
0.4628 | 10850 | 0.0 | - |
0.4649 | 10900 | 0.0 | - |
0.4671 | 10950 | 0.0001 | - |
0.4692 | 11000 | 0.0 | - |
0.4713 | 11050 | 0.1696 | - |
0.4735 | 11100 | 0.0 | - |
0.4756 | 11150 | 0.1243 | - |
0.4777 | 11200 | 0.0 | - |
0.4799 | 11250 | 0.0 | - |
0.4820 | 11300 | 0.0003 | - |
0.4841 | 11350 | 0.0707 | - |
0.4863 | 11400 | 0.166 | - |
0.4884 | 11450 | 0.4964 | - |
0.4905 | 11500 | 0.0023 | - |
0.4927 | 11550 | 0.0 | - |
0.4948 | 11600 | 0.0 | - |
0.4969 | 11650 | 0.173 | - |
0.4991 | 11700 | 0.0 | - |
0.5012 | 11750 | 0.0004 | - |
0.5033 | 11800 | 0.0 | - |
0.5055 | 11850 | 0.125 | - |
0.5076 | 11900 | 0.0042 | - |
0.5097 | 11950 | 0.012 | - |
0.5119 | 12000 | 0.0046 | - |
0.5140 | 12050 | 0.0001 | - |
0.5161 | 12100 | 0.0062 | - |
0.5183 | 12150 | 0.0 | - |
0.5204 | 12200 | 0.017 | - |
0.5225 | 12250 | 0.2668 | - |
0.5247 | 12300 | 0.0986 | - |
0.5268 | 12350 | 0.0071 | - |
0.5289 | 12400 | 0.0055 | - |
0.5311 | 12450 | 0.006 | - |
0.5332 | 12500 | 0.0057 | - |
0.5353 | 12550 | 0.0044 | - |
0.5375 | 12600 | 0.0039 | - |
0.5396 | 12650 | 0.1685 | - |
0.5417 | 12700 | 0.125 | - |
0.5438 | 12750 | 0.0026 | - |
0.5460 | 12800 | 0.0 | - |
0.5481 | 12850 | 0.0 | - |
0.5502 | 12900 | 0.1024 | - |
0.5524 | 12950 | 0.0 | - |
0.5545 | 13000 | 0.0 | - |
0.5566 | 13050 | 0.0083 | - |
0.5588 | 13100 | 0.0 | - |
0.5609 | 13150 | 0.0001 | - |
0.5630 | 13200 | 0.0 | - |
0.5652 | 13250 | 0.095 | - |
0.5673 | 13300 | 0.0001 | - |
0.5694 | 13350 | 0.0026 | - |
0.5716 | 13400 | 0.0 | - |
0.5737 | 13450 | 0.0041 | - |
0.5758 | 13500 | 0.1654 | - |
0.5780 | 13550 | 0.0003 | - |
0.5801 | 13600 | 0.0056 | - |
0.5822 | 13650 | 0.0 | - |
0.5844 | 13700 | 0.1012 | - |
0.5865 | 13750 | 0.0 | - |
0.5886 | 13800 | 0.0001 | - |
0.5908 | 13850 | 0.0042 | - |
0.5929 | 13900 | 0.0122 | - |
0.5950 | 13950 | 0.1047 | - |
0.5972 | 14000 | 0.0 | - |
0.5993 | 14050 | 0.0121 | - |
0.6014 | 14100 | 0.0 | - |
0.6036 | 14150 | 0.0 | - |
0.6057 | 14200 | 0.0 | - |
0.6078 | 14250 | 0.0105 | - |
0.6100 | 14300 | 0.0 | - |
0.6121 | 14350 | 0.011 | - |
0.6142 | 14400 | 0.0329 | - |
0.6164 | 14450 | 0.0942 | - |
0.6185 | 14500 | 0.0173 | - |
0.6206 | 14550 | 0.0 | - |
0.6228 | 14600 | 0.1032 | - |
0.6249 | 14650 | 0.016 | - |
0.6270 | 14700 | 0.0079 | - |
0.6292 | 14750 | 0.0 | - |
0.6313 | 14800 | 0.1088 | - |
0.6334 | 14850 | 0.0091 | - |
0.6356 | 14900 | 0.0039 | - |
0.6377 | 14950 | 0.0 | - |
0.6398 | 15000 | 0.0 | - |
0.6420 | 15050 | 0.0 | - |
0.6441 | 15100 | 0.1654 | - |
0.6462 | 15150 | 0.0 | - |
0.6484 | 15200 | 0.0002 | - |
0.6505 | 15250 | 0.0 | - |
0.6526 | 15300 | 0.1745 | - |
0.6548 | 15350 | 0.0 | - |
0.6569 | 15400 | 0.156 | - |
0.6590 | 15450 | 0.0 | - |
0.6611 | 15500 | 0.0 | - |
0.6633 | 15550 | 0.1755 | - |
0.6654 | 15600 | 0.008 | - |
0.6675 | 15650 | 0.0 | - |
0.6697 | 15700 | 0.0 | - |
0.6718 | 15750 | 0.0041 | - |
0.6739 | 15800 | 0.0037 | - |
0.6761 | 15850 | 0.0 | - |
0.6782 | 15900 | 0.0 | - |
0.6803 | 15950 | 0.0092 | - |
0.6825 | 16000 | 0.0071 | - |
0.6846 | 16050 | 0.0053 | - |
0.6867 | 16100 | 0.0 | - |
0.6889 | 16150 | 0.004 | - |
0.6910 | 16200 | 0.0036 | - |
0.6931 | 16250 | 0.0 | - |
0.6953 | 16300 | 0.0 | - |
0.6974 | 16350 | 0.184 | - |
0.6995 | 16400 | 0.0 | - |
0.7017 | 16450 | 0.0133 | - |
0.7038 | 16500 | 0.0 | - |
0.7059 | 16550 | 0.174 | - |
0.7081 | 16600 | 0.0 | - |
0.7102 | 16650 | 0.0233 | - |
0.7123 | 16700 | 0.0117 | - |
0.7145 | 16750 | 0.0272 | - |
0.7166 | 16800 | 0.0095 | - |
0.7187 | 16850 | 0.0 | - |
0.7209 | 16900 | 0.1656 | - |
0.7230 | 16950 | 0.0055 | - |
0.7251 | 17000 | 0.0 | - |
0.7273 | 17050 | 0.1716 | - |
0.7294 | 17100 | 0.0 | - |
0.7315 | 17150 | 0.0 | - |
0.7337 | 17200 | 0.1035 | - |
0.7358 | 17250 | 0.0694 | - |
0.7379 | 17300 | 0.1733 | - |
0.7401 | 17350 | 0.0092 | - |
0.7422 | 17400 | 0.1656 | - |
0.7443 | 17450 | 0.0 | - |
0.7465 | 17500 | 0.1655 | - |
0.7486 | 17550 | 0.0059 | - |
0.7507 | 17600 | 0.1116 | - |
0.7529 | 17650 | 0.0 | - |
0.7550 | 17700 | 0.0068 | - |
0.7571 | 17750 | 0.0053 | - |
0.7593 | 17800 | 0.0 | - |
0.7614 | 17850 | 0.0062 | - |
0.7635 | 17900 | 0.0104 | - |
0.7657 | 17950 | 0.1727 | - |
0.7678 | 18000 | 0.0 | - |
0.7699 | 18050 | 0.0 | - |
0.7721 | 18100 | 0.0 | - |
0.7742 | 18150 | 0.0714 | - |
0.7763 | 18200 | 0.0 | - |
0.7785 | 18250 | 0.0 | - |
0.7806 | 18300 | 0.0002 | - |
0.7827 | 18350 | 0.0 | - |
0.7848 | 18400 | 0.0 | - |
0.7870 | 18450 | 0.0996 | - |
0.7891 | 18500 | 0.0 | - |
0.7912 | 18550 | 0.0 | - |
0.7934 | 18600 | 0.0139 | - |
0.7955 | 18650 | 0.0 | - |
0.7976 | 18700 | 0.1701 | - |
0.7998 | 18750 | 0.0 | - |
0.8019 | 18800 | 0.0001 | - |
0.8040 | 18850 | 0.0 | - |
0.8062 | 18900 | 0.0 | - |
0.8083 | 18950 | 0.0 | - |
0.8104 | 19000 | 0.0 | - |
0.8126 | 19050 | 0.0 | - |
0.8147 | 19100 | 0.1093 | - |
0.8168 | 19150 | 0.0 | - |
0.8190 | 19200 | 0.0 | - |
0.8211 | 19250 | 0.0075 | - |
0.8232 | 19300 | 0.1079 | - |
0.8254 | 19350 | 0.0112 | - |
0.8275 | 19400 | 0.1655 | - |
0.8296 | 19450 | 0.0152 | - |
0.8318 | 19500 | 0.1152 | - |
0.8339 | 19550 | 0.0 | - |
0.8360 | 19600 | 0.0 | - |
0.8382 | 19650 | 0.0079 | - |
0.8403 | 19700 | 0.0 | - |
0.8424 | 19750 | 0.0 | - |
0.8446 | 19800 | 0.0 | - |
0.8467 | 19850 | 0.0 | - |
0.8488 | 19900 | 0.1161 | - |
0.8510 | 19950 | 0.0057 | - |
0.8531 | 20000 | 0.0 | - |
0.8552 | 20050 | 0.0046 | - |
0.8574 | 20100 | 0.0 | - |
0.8595 | 20150 | 0.0068 | - |
0.8616 | 20200 | 0.0 | - |
0.8638 | 20250 | 0.0 | - |
0.8659 | 20300 | 0.0 | - |
0.8680 | 20350 | 0.0 | - |
0.8702 | 20400 | 0.0141 | - |
0.8723 | 20450 | 0.0001 | - |
0.8744 | 20500 | 0.0 | - |
0.8766 | 20550 | 0.0 | - |
0.8787 | 20600 | 0.0171 | - |
0.8808 | 20650 | 0.0 | - |
0.8830 | 20700 | 0.0 | - |
0.8851 | 20750 | 0.0077 | - |
0.8872 | 20800 | 0.0 | - |
0.8894 | 20850 | 0.0 | - |
0.8915 | 20900 | 0.0 | - |
0.8936 | 20950 | 0.0 | - |
0.8958 | 21000 | 0.0 | - |
0.8979 | 21050 | 0.0 | - |
0.9000 | 21100 | 0.0 | - |
0.9021 | 21150 | 0.0 | - |
0.9043 | 21200 | 0.0 | - |
0.9064 | 21250 | 0.1048 | - |
0.9085 | 21300 | 0.006 | - |
0.9107 | 21350 | 0.0 | - |
0.9128 | 21400 | 0.0 | - |
0.9149 | 21450 | 0.005 | - |
0.9171 | 21500 | 0.0 | - |
0.9192 | 21550 | 0.0325 | - |
0.9213 | 21600 | 0.0136 | - |
0.9235 | 21650 | 0.0 | - |
0.9256 | 21700 | 0.0062 | - |
0.9277 | 21750 | 0.1656 | - |
0.9299 | 21800 | 0.1648 | - |
0.9320 | 21850 | 0.0 | - |
0.9341 | 21900 | 0.0 | - |
0.9363 | 21950 | 0.0 | - |
0.9384 | 22000 | 0.2844 | - |
0.9405 | 22050 | 0.0 | - |
0.9427 | 22100 | 0.0 | - |
0.9448 | 22150 | 0.0 | - |
0.9469 | 22200 | 0.0 | - |
0.9491 | 22250 | 0.0 | - |
0.9512 | 22300 | 0.2096 | - |
0.9533 | 22350 | 0.0073 | - |
0.9555 | 22400 | 0.006 | - |
0.9576 | 22450 | 0.0 | - |
0.9597 | 22500 | 0.0079 | - |
0.9619 | 22550 | 0.0071 | - |
0.9640 | 22600 | 0.0 | - |
0.9661 | 22650 | 0.006 | - |
0.9683 | 22700 | 0.1048 | - |
0.9704 | 22750 | 0.007 | - |
0.9725 | 22800 | 0.0 | - |
0.9747 | 22850 | 0.0 | - |
0.9768 | 22900 | 0.007 | - |
0.9789 | 22950 | 0.0 | - |
0.9811 | 23000 | 0.1049 | - |
0.9832 | 23050 | 0.0069 | - |
0.9853 | 23100 | 0.0 | - |
0.9875 | 23150 | 0.0 | - |
0.9896 | 23200 | 0.0 | - |
0.9917 | 23250 | 0.0 | - |
0.9939 | 23300 | 0.007 | - |
0.9960 | 23350 | 0.0147 | - |
0.9981 | 23400 | 0.0 | - |
Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- spaCy: 3.7.4
- Transformers: 4.36.2
- PyTorch: 2.1.2
- Datasets: 2.18.0
- Tokenizers: 0.15.2
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}
}