Instructions to use textattack/xlnet-large-cased-MRPC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/xlnet-large-cased-MRPC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/xlnet-large-cased-MRPC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/xlnet-large-cased-MRPC") model = AutoModelForSequenceClassification.from_pretrained("textattack/xlnet-large-cased-MRPC") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "XLNetForSequenceClassification" | |
| ], | |
| "attn_type": "bi", | |
| "bi_data": false, | |
| "bos_token_id": 1, | |
| "clamp_len": -1, | |
| "d_head": 64, | |
| "d_inner": 4096, | |
| "d_model": 1024, | |
| "dropout": 0.1, | |
| "end_n_top": 5, | |
| "eos_token_id": 2, | |
| "ff_activation": "gelu", | |
| "finetuning_task": "mrpc", | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-12, | |
| "mem_len": null, | |
| "model_type": "xlnet", | |
| "n_head": 16, | |
| "n_layer": 24, | |
| "pad_token_id": 5, | |
| "reuse_len": null, | |
| "same_length": false, | |
| "start_n_top": 5, | |
| "summary_activation": "tanh", | |
| "summary_last_dropout": 0.1, | |
| "summary_type": "last", | |
| "summary_use_proj": true, | |
| "task_specific_params": { | |
| "text-generation": { | |
| "do_sample": true, | |
| "max_length": 250 | |
| } | |
| }, | |
| "untie_r": true, | |
| "vocab_size": 32000 | |
| } | |