Feature Extraction
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use comet24082002/finetuned_bge_ver35_Round2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use comet24082002/finetuned_bge_ver35_Round2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="comet24082002/finetuned_bge_ver35_Round2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("comet24082002/finetuned_bge_ver35_Round2") model = AutoModel.from_pretrained("comet24082002/finetuned_bge_ver35_Round2") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "/kaggle/working/finetuned_bgem3_ver35_Round2_model", | |
| "architectures": [ | |
| "XLMRobertaModel" | |
| ], | |
| "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": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 8194, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.41.2", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |