Text Classification
Transformers
Safetensors
English
bert
finance
accounting
innovation
multi-label-classification
text-embeddings-inference
Instructions to use mustafahci/InnoBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mustafahci/InnoBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mustafahci/InnoBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mustafahci/InnoBERT") model = AutoModelForSequenceClassification.from_pretrained("mustafahci/InnoBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download evaluation_report.csv from mustafahci/InnoBERT: direct link, hf CLI and curl.
- Browser
- Download file 977 Bytes
-
https://huggingface.co/mustafahci/InnoBERT/resolve/main/evaluation_report.csv
- Command line
-
hf download hf://mustafahci/InnoBERT/evaluation_report.csv
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curl -L -o evaluation_report.csv https://huggingface.co/mustafahci/InnoBERT/resolve/main/evaluation_report.csv
977 Bytes
| ,precision,recall,f1-score,support | |
| inno_product,0.734020618556701,0.836335160532498,0.7818448023426061,16601.0 | |
| inno_process,0.6556559675911499,0.5145512350207875,0.5765963277610304,4089.0 | |
| inno_organizational,0.6187234042553191,0.4879194630872483,0.5455909943714822,1490.0 | |
| inno_marketing,0.7047619047619048,0.6948356807511737,0.6997635933806147,1278.0 | |
| inno_businessmodel,0.6337157987643425,0.4802675585284281,0.5464231354642314,1495.0 | |
| inno_sustainability,0.7203252032520325,0.6582466567607727,0.687888198757764,1346.0 | |
| inno_AI,0.6460905349794238,0.40568475452196384,0.4984126984126984,387.0 | |
| inno_uncategorized,0.9126196193684815,0.8099949102939306,0.8582503328445995,31436.0 | |
| micro avg,0.8140594922456689,0.7712570111145521,0.7920804325546877,58122.0 | |
| macro avg,0.7032391314411695,0.6109794274371003,0.6493462604168783,58122.0 | |
| weighted avg,0.8180232504215391,0.7712570111145521,0.790750140781355,58122.0 | |
| samples avg,0.7861368542837037,0.7809037344497322,0.7801511193807326,58122.0 | |