Instructions to use tner/bertweet-base-tweetner7-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tner/bertweet-base-tweetner7-random with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tner/bertweet-base-tweetner7-random")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tner/bertweet-base-tweetner7-random") model = AutoModelForTokenClassification.from_pretrained("tner/bertweet-base-tweetner7-random", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"dataset": ["tner/tweetner7"], "dataset_split": "train_random", "dataset_name": null, "local_dataset": null, "model": "vinai/bertweet-base", "crf": true, "max_length": 128, "epoch": 30, "batch_size": 32, "lr": 0.0001, "random_seed": 0, "gradient_accumulation_steps": 1, "weight_decay": 1e-07, "lr_warmup_step_ratio": 0.3, "max_grad_norm": 1} |