Instructions to use castorini/afriberta_v2_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/afriberta_v2_large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="castorini/afriberta_v2_large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("castorini/afriberta_v2_large") model = AutoModelForMaskedLM.from_pretrained("castorini/afriberta_v2_large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "/u6/aooladip/aooladip/projects/afriberta-v2/experiments/afriberta_v2_large_new/checkpoint-524288", | |
| "architectures": [ | |
| "XLMRobertaForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-05, | |
| "max_length": 512, | |
| "max_position_embeddings": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 6, | |
| "num_hidden_layers": 10, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.45.2", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 150002 | |
| } | |