Feature Extraction
Transformers
PyTorch
Vietnamese
xlm-roberta
vietnamese
contrastive-learning
sentence-embedding
natural-language-inference
low-resource
nlu
Instructions to use huynhtin/ViCLSR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huynhtin/ViCLSR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="huynhtin/ViCLSR")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, XLMRobertaForCL tokenizer = AutoTokenizer.from_pretrained("huynhtin/ViCLSR") model = XLMRobertaForCL.from_pretrained("huynhtin/ViCLSR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from huynhtin/ViCLSR: direct link, hf CLI and curl.
- Browser
- Download file 706 Bytes
-
https://huggingface.co/huynhtin/ViCLSR/resolve/refs%2Fpr%2F5/config.json
- Command line
-
hf download hf://huynhtin/ViCLSR@refs/pr/5/config.json
-
curl -L -o config.json https://huggingface.co/huynhtin/ViCLSR/resolve/refs%2Fpr%2F5/config.json
706 Bytes
| { | |
| "_name_or_path": "result/ViNLI-XNLI/xlm-roberta-large-hnw1-fix", | |
| "architectures": [ | |
| "XLMRobertaForCL" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "eos_token_id": 2, | |
| "gradient_checkpointing": false, | |
| "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": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.2.1", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |