Instructions to use tweettemposhift/ner-ner_random2_seed2-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_seed2-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_seed2-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tweettemposhift/ner-ner_random2_seed2-roberta-base") model = AutoModelForTokenClassification.from_pretrained("tweettemposhift/ner-ner_random2_seed2-roberta-base") - Notebooks
- Google Colab
- Kaggle
| {"test/eval_loss": 0.3835185766220093, "test/eval_corporation": {"precision": 0.5337748344370861, "recall": 0.47411764705882353, "f1": 0.5021806853582554, "number": 850}, "test/eval_creative_work": {"precision": 0.4485981308411215, "recall": 0.4201312910284464, "f1": 0.4338983050847458, "number": 457}, "test/eval_event": {"precision": 0.5198300283286119, "recall": 0.49796472184531887, "f1": 0.5086625086625087, "number": 737}, "test/eval_group": {"precision": 0.6624579124579124, "recall": 0.6715017064846417, "f1": 0.6669491525423727, "number": 1172}, "test/eval_location": {"precision": 0.6418918918918919, "recall": 0.6666666666666666, "f1": 0.6540447504302925, "number": 285}, "test/eval_person": {"precision": 0.8683442742523705, "recall": 0.8782736997417927, "f1": 0.8732807628828168, "number": 2711}, "test/eval_product": {"precision": 0.6864175022789426, "recall": 0.7339181286549707, "f1": 0.7093735280263779, "number": 1026}, "test/eval_overall_precision": 0.7034109816971714, "test/eval_overall_recall": 0.7008842221608179, "test/eval_overall_f1": 0.7021453287197232, "test/eval_overall_accuracy": 0.8889929157694348, "test/eval_runtime": 3.9754, "test/eval_samples_per_second": 176.336, "test/eval_steps_per_second": 22.136} |