The model 'FastFit' is not supported for text-classification


model = FastFit.from_pretrained("fast-fit")
model

gives

FastFit(
  (encoder): MPNetModel(
    (embeddings): MPNetEmbeddings(
      (word_embeddings): Embedding(30527, 768, padding_idx=1)
      (position_embeddings): Embedding(514, 768, padding_idx=1)
      (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      (dropout): Dropout(p=0.1, inplace=False)
    )
    (encoder): MPNetEncoder(
      (layer): ModuleList(
        (0-11): 12 x MPNetLayer(
          (attention): MPNetAttention(
            (attn): MPNetSelfAttention(
              (q): Linear(in_features=768, out_features=768, bias=True)
              (k): Linear(in_features=768, out_features=768, bias=True)
              (v): Linear(in_features=768, out_features=768, bias=True)
              (o): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (intermediate): MPNetIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): MPNetOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
      )
      (relative_attention_bias): Embedding(32, 12)
    )
    (pooler): MPNetPooler(
      (dense): Linear(in_features=768, out_features=768, bias=True)
      (activation): Tanh()
    )
  )
  (projection): Linear(in_features=768, out_features=128, bias=False)
  (clf): Linear(in_features=768, out_features=17999, bias=True)
  (dropout): Dropout(p=0.1, inplace=False)
  (batch_norm): BatchNorm1d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (clf_criterion): CrossEntropyLoss()
  (sim_criterion): SupConLoss()
  (all_docs): ParameterList(
      (0): Parameter containing: [torch.int64 of size 17999x10]
      (1): Parameter containing: [torch.int64 of size 17999x10]
  )
)

from transformers import AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/paraphrase-mpnet-base-v2")
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
print(classifier("Cataract"))

throws


[ERROR|base.py:1090] 2024-06-25 15:08:16,096 >> The model 'FastFit' is not supported for text-classification. Supported models are ['AlbertForSequenceClassification', 'BartForSequenceClassification', 'BertForSequenceClassification', 'BigBirdForSequenceClassification', 'BigBirdPegasusForSequenceClassification', 'BioGptForSequenceClassification', 'BloomForSequenceClassification', 'CamembertForSequenceClassification', 'CanineForSequenceClassification', 'LlamaForSequenceClassification', 'ConvBertForSequenceClassification', 'CTRLForSequenceClassification', 'Data2VecTextForSequenceClassification', 'DebertaForSequenceClassification', 'DebertaV2ForSequenceClassification', 'DistilBertForSequenceClassification', 'ElectraForSequenceClassification', 'ErnieForSequenceClassification', 'ErnieMForSequenceClassification', 'EsmForSequenceClassification', 'FalconForSequenceClassification', 'FlaubertForSequenceClassification', 'FNetForSequenceClassification', 'FunnelForSequenceClassification', 'GemmaForSequenceClassification', 'GPT2ForSequenceClassification', 'GPT2ForSequenceClassification', 'GPTBigCodeForSequenceClassification', 'GPTNeoForSequenceClassification', 'GPTNeoXForSequenceClassification', 'GPTJForSequenceClassification', 'IBertForSequenceClassification', 'JambaForSequenceClassification', 'JetMoeForSequenceClassification', 'LayoutLMForSequenceClassification', 'LayoutLMv2ForSequenceClassification', 'LayoutLMv3ForSequenceClassification', 'LEDForSequenceClassification', 'LiltForSequenceClassification', 'LlamaForSequenceClassification', 'LongformerForSequenceClassification', 'LukeForSequenceClassification', 'MarkupLMForSequenceClassification', 'MBartForSequenceClassification', 'MegaForSequenceClassification', 'MegatronBertForSequenceClassification', 'MistralForSequenceClassification', 'MixtralForSequenceClassification', 'MobileBertForSequenceClassification', 'MPNetForSequenceClassification', 'MptForSequenceClassification', 'MraForSequenceClassification', 'MT5ForSequenceClassification', 'MvpForSequenceClassification', 'NezhaForSequenceClassification', 'NystromformerForSequenceClassification', 'OpenLlamaForSequenceClassification', 'OpenAIGPTForSequenceClassification', 'OPTForSequenceClassification', 'PerceiverForSequenceClassification', 'PersimmonForSequenceClassification', 'PhiForSequenceClassification', 'Phi3ForSequenceClassification', 'PLBartForSequenceClassification', 'QDQBertForSequenceClassification', 'Qwen2ForSequenceClassification', 'Qwen2MoeForSequenceClassification', 'ReformerForSequenceClassification', 'RemBertForSequenceClassification', 'RobertaForSequenceClassification', 'RobertaPreLayerNormForSequenceClassification', 'RoCBertForSequenceClassification', 'RoFormerForSequenceClassification', 'SqueezeBertForSequenceClassification', 'StableLmForSequenceClassification', 'Starcoder2ForSequenceClassification', 'T5ForSequenceClassification', 'TapasForSequenceClassification', 'TransfoXLForSequenceClassification', 'UMT5ForSequenceClassification', 'XLMForSequenceClassification', 'XLMRobertaForSequenceClassification', 'XLMRobertaXLForSequenceClassification', 'XLNetForSequenceClassification', 'XmodForSequenceClassification', 'YosoForSequenceClassification'].

IBM / fastfit

The model 'FastFit' is not supported for text-classification #20