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review-aspects-data-balanced-medium
bziemba/review-aspects-data-balanced-medium
review-aspects-data-balanced-medium is a machine learning model from bziemba. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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At a glance
- Library
- transformers
- Model type
- xlm-roberta
- Access
- Public
- Created
- Jan 8, 2026
- Updated
- Jan 8, 2026
- SHA
- 9fcc1fbe
Model card
From the Hugging Face model README
================================================================================
ASPEKT | ACC | F1-MACRO | WYNIK
================================================================================
overall_experience | 0.8406 | 0.7705 | (Neg/None/Pos)
location | 0.9468 | 0.8534 | (Neg/None/Pos)
cleanliness | 0.9548 | 0.9243 | (Neg/None/Pos)
bed_comfort | 0.8925 | 0.8122 | (Neg/None/Pos)
size_space | 0.8034 | 0.7348 | (Neg/None/Pos)
kitchen | 0.8914 | 0.8038 | (Neg/None/Pos)
wifi | 0.9888 | 0.8632 | (Neg/None/Pos)
noise | 0.9588 | 0.9216 | (Neg/None/Pos)
safety | 0.9018 | 0.6855 | (Neg/None/Pos)
host_contact | 0.9553 | 0.9265 | (Neg/None/Pos)
value_for_money | 0.9350 | 0.7852 | (Neg/None/Pos)
food | 0.8778 | 0.6203 | (Neg/None/Pos)
--------------------------------------------------------------------------------
>>> SZCZEGÓŁY DLA overall_experience
precision recall f1-score support
Negatywny 0.89 0.86 0.87 423
Brak/OK 0.39 0.83 0.53 403
Pozytywny 0.99 0.84 0.91 2913
accuracy 0.84 3739
macro avg 0.75 0.84 0.77 3739
weighted avg 0.91 0.84 0.86 3739
>>> SZCZEGÓŁY DLA location
precision recall f1-score support
Negatywny 0.49 0.96 0.65 92
Brak/OK 0.96 0.92 0.94 1301
Pozytywny 0.97 0.96 0.97 2346
accuracy 0.95 3739
macro avg 0.81 0.95 0.85 3739
weighted avg 0.96 0.95 0.95 3739
>>> SZCZEGÓŁY DLA cleanliness
precision recall f1-score support
Negatywny 0.79 0.92 0.85 302
Brak/OK 0.98 0.96 0.97 2392
Pozytywny 0.96 0.95 0.96 1045
accuracy 0.95 3739
macro avg 0.91 0.94 0.92 3739
weighted avg 0.96 0.95 0.96 3739
>>> SZCZEGÓŁY DLA bed_comfort
precision recall f1-score support
Negatywny 0.63 0.95 0.76 171
Brak/OK 0.99 0.88 0.93 3102
Pozytywny 0.61 0.95 0.74 466
accuracy 0.89 3739
macro avg 0.74 0.93 0.81 3739
weighted avg 0.93 0.89 0.90 3739
>>> SZCZEGÓŁY DLA size_space
precision recall f1-score support
Negatywny 0.54 0.94 0.68 258
Brak/OK 0.94 0.80 0.86 2801
Pozytywny 0.58 0.76 0.66 680
accuracy 0.80 3739
macro avg 0.68 0.83 0.73 3739
weighted avg 0.85 0.80 0.81 3739
>>> SZCZEGÓŁY DLA kitchen
precision recall f1-score support
Negatywny 0.61 0.96 0.74 164
Brak/OK 0.99 0.88 0.93 3109
Pozytywny 0.61 0.92 0.73 466
accuracy 0.89 3739
macro avg 0.74 0.92 0.80 3739
weighted avg 0.93 0.89 0.90 3739
>>> SZCZEGÓŁY DLA wifi
precision recall f1-score support
Negatywny 0.86 0.98 0.92 55
Brak/OK 1.00 0.99 0.99 3648
Pozytywny 0.53 0.94 0.68 36
accuracy 0.99 3739
macro avg 0.80 0.97 0.86 3739
weighted avg 0.99 0.99 0.99 3739
>>> SZCZEGÓŁY DLA noise
precision recall f1-score support
Negatywny 0.81 0.92 0.86 305
Brak/OK 0.99 0.97 0.98 2881
Pozytywny 0.92 0.94 0.93 553
accuracy 0.96 3739
macro avg 0.90 0.94 0.92 3739
weighted avg 0.96 0.96 0.96 3739
>>> SZCZEGÓŁY DLA safety
precision recall f1-score support
Negatywny 0.45 0.92 0.60 111
Brak/OK 0.99 0.90 0.95 3490
Pozytywny 0.36 0.88 0.51 138
accuracy 0.90 3739
macro avg 0.60 0.90 0.69 3739
weighted avg 0.95 0.90 0.92 3739
>>> SZCZEGÓŁY DLA host_contact
precision recall f1-score support
Negatywny 0.77 0.97 0.86 203
Brak/OK 0.97 0.93 0.95 1568
Pozytywny 0.97 0.97 0.97 1968
accuracy 0.96 3739
macro avg 0.90 0.96 0.93 3739
weighted avg 0.96 0.96 0.96 3739
>>> SZCZEGÓŁY DLA value_for_money
precision recall f1-score support
Negatywny 0.43 0.92 0.59 106
Brak/OK 0.99 0.94 0.96 3357
Pozytywny 0.76 0.86 0.81 276
accuracy 0.94 3739
macro avg 0.73 0.90 0.79 3739
weighted avg 0.95 0.94 0.94 3739
>>> SZCZEGÓŁY DLA food
precision recall f1-score support
Negatywny 0.21 0.95 0.35 19
Brak/OK 0.99 0.87 0.93 3424
Pozytywny 0.42 0.93 0.58 296
accuracy 0.88 3739
macro avg 0.54 0.92 0.62 3739
weighted avg 0.94 0.88 0.90 3739
Model Details
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- Library
- transformers
- Type
- xlm-roberta
- Created
- Jan 8, 2026
- Updated
- Jan 8, 2026