Instructions to use tweettemposhift/ner-ner_random2_seed0-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tweettemposhift/ner-ner_random2_seed0-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tweettemposhift/ner-ner_random2_seed0-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tweettemposhift/ner-ner_random2_seed0-roberta-base") model = AutoModelForTokenClassification.from_pretrained("tweettemposhift/ner-ner_random2_seed0-roberta-base") - Notebooks
- Google Colab
- Kaggle
| {"test/eval_loss": 0.39088281989097595, "test/eval_corporation": {"precision": 0.5621156211562116, "recall": 0.5376470588235294, "f1": 0.5496091401082381, "number": 850}, "test/eval_creative_work": {"precision": 0.3877005347593583, "recall": 0.3172866520787746, "f1": 0.3489771359807461, "number": 457}, "test/eval_event": {"precision": 0.5757121439280359, "recall": 0.5210312075983717, "f1": 0.547008547008547, "number": 737}, "test/eval_group": {"precision": 0.6755593803786575, "recall": 0.6697952218430034, "f1": 0.6726649528706085, "number": 1172}, "test/eval_location": {"precision": 0.6188811188811189, "recall": 0.6210526315789474, "f1": 0.6199649737302978, "number": 285}, "test/eval_person": {"precision": 0.8735415882574332, "recall": 0.8561416451493914, "f1": 0.8647540983606558, "number": 2711}, "test/eval_product": {"precision": 0.6434316353887399, "recall": 0.7017543859649122, "f1": 0.6713286713286714, "number": 1026}, "test/eval_overall_precision": 0.7048601299802204, "test/eval_overall_recall": 0.6892788063000829, "test/eval_overall_f1": 0.6969823973176864, "test/eval_overall_accuracy": 0.8884623786786505, "test/eval_runtime": 4.0479, "test/eval_samples_per_second": 173.175, "test/eval_steps_per_second": 21.74} |