Instructions to use luqh/ClinicalT5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luqh/ClinicalT5-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("luqh/ClinicalT5-base") model = AutoModelForSeq2SeqLM.from_pretrained("luqh/ClinicalT5-base") - Notebooks
- Google Colab
- Kaggle
error : ValueError: Unrecognized configuration class
ValueError: Unrecognized configuration class <class
'transformers.models.t5.configuration_t5.T5Config'> for this kind of AutoModel:
AutoModelForTokenClassification.
Model type should be one of AlbertConfig, BertConfig, BigBirdConfig,
BioGptConfig, BloomConfig, CamembertConfig, CanineConfig, ConvBertConfig,
Data2VecTextConfig, DebertaConfig, DebertaV2Config, DistilBertConfig,
ElectraConfig, ErnieConfig, ErnieMConfig, EsmConfig, FlaubertConfig, FNetConfig,
FunnelConfig, GPT2Config, GPT2Config, GPTBigCodeConfig, GPTNeoConfig,
GPTNeoXConfig, IBertConfig, LayoutLMConfig, LayoutLMv2Config, LayoutLMv3Config,
LiltConfig, LongformerConfig, LukeConfig, MarkupLMConfig, MegaConfig,
MegatronBertConfig, MobileBertConfig, MPNetConfig, NezhaConfig,
NystromformerConfig, QDQBertConfig, RemBertConfig, RobertaConfig,
RobertaPreLayerNormConfig, RoCBertConfig, RoFormerConfig, SqueezeBertConfig,
XLMConfig, XLMRobertaConfig, XLMRobertaXLConfig, XLNetConfig, XmodConfig,
YosoConfig.