Zero-Shot Classification
Transformers
PyTorch
102 languages
T5
classification
information-extraction
zero-shot
Inference Endpoints
File size: 1,228 Bytes
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{
  "architectures": [
    "T5ForZeroShotClassification"
  ],
  "classifier_dropout": 0.0,
  "d_ff": 2048,
  "d_kv": 64,
  "d_model": 768,
  "decoder_start_token_id": 0,
  "dense_act_fn": "gelu_new",
  "dropout_rate": 0.1,
  "encoder_attention_type": "block",
  "eos_token_id": 1,
  "feed_forward_proj": "gated-gelu",
  "global_block_size": 128,
  "id2label": {
    "0": "contradiction",
    "1": "entailment",
    "2": "neutral"
  },
  "initializer_factor": 1.0,
  "is_encoder_decoder": true,
  "is_gated_act": true,
  "label2id": {
    "contradiction": 0,
    "entailment": 1,
    "neutral": 2
  },
  "layer_norm_epsilon": 1e-06,
  "loss_type": "cross_entropy",
  "model_type": "T5",
  "naive_attention": true,
  "num_decoder_layers": 12,
  "num_heads": 12,
  "num_layers": 12,
  "output_past": true,
  "pad_token_id": 0,
  "positional_embedding_type": "relative",
  "problem_type": "single_label_classification",
  "relative_attention_max_distance": 128,
  "relative_attention_num_buckets": 32,
  "residuals": "pre",
  "sliding_window": 128,
  "tie_word_embeddings": false,
  "tokenizer_class": "T5Tokenizer",
  "torch_dtype": "float32",
  "transformers_version": "4.33.0.dev0",
  "use_cache": true,
  "vocab_size": 250112
}