transformers.js/pr_1649/en/api/models.md

models

Definitions of all models available in Transformers.js.

Example: Load and run an AutoModel.

import { AutoModel, AutoTokenizer } from '@huggingface/transformers';

const tokenizer = await AutoTokenizer.from_pretrained('Xenova/bert-base-uncased');
const model = await AutoModel.from_pretrained('Xenova/bert-base-uncased');

const inputs = await tokenizer('I love transformers!');
const { logits } = await model(inputs);
// Tensor {
//     data: Float32Array(183132) [-7.117443084716797, -7.107812881469727, -7.092104911804199, ...]
//     dims: (3) [1, 6, 30522],
//     type: "float32",
//     size: 183132,
// }

We also provide other AutoModels (listed below), which you can use in the same way as the Python library. For example:

Example: Load and run an AutoModelForSeq2SeqLM.

import { AutoModelForSeq2SeqLM, AutoTokenizer } from '@huggingface/transformers';

const tokenizer = await AutoTokenizer.from_pretrained('Xenova/t5-small');
const model = await AutoModelForSeq2SeqLM.from_pretrained('Xenova/t5-small');

const { input_ids } = await tokenizer('translate English to German: I love transformers!');
const outputs = await model.generate(input_ids);
const decoded = tokenizer.decode(outputs[0], { skip_special_tokens: true });
// 'Ich liebe Transformatoren!'

• models
  • static
    • .AutoModel
      • new AutoModel()
      • .MODEL_CLASS_MAPPINGS : Array.<Map>
    • .AutoModelForSequenceClassification
      • new AutoModelForSequenceClassification()
    • .AutoModelForTokenClassification
      • new AutoModelForTokenClassification()
    • .AutoModelForSeq2SeqLM
      • new AutoModelForSeq2SeqLM()
    • .AutoModelForSpeechSeq2Seq
      • new AutoModelForSpeechSeq2Seq()
    • .AutoModelForTextToSpectrogram
      • new AutoModelForTextToSpectrogram()
    • .AutoModelForTextToWaveform
      • new AutoModelForTextToWaveform()
    • .AutoModelForCausalLM
      • new AutoModelForCausalLM()
    • .AutoModelForMaskedLM
      • new AutoModelForMaskedLM()
    • .AutoModelForQuestionAnswering
      • new AutoModelForQuestionAnswering()
    • .AutoModelForVision2Seq
      • new AutoModelForVision2Seq()
    • .AutoModelForImageClassification
      • new AutoModelForImageClassification()
    • .AutoModelForImageSegmentation
      • new AutoModelForImageSegmentation()
    • .AutoModelForSemanticSegmentation
      • new AutoModelForSemanticSegmentation()
    • .AutoModelForUniversalSegmentation
      • new AutoModelForUniversalSegmentation()
    • .AutoModelForObjectDetection
      • new AutoModelForObjectDetection()
    • .AutoModelForMaskGeneration
      • new AutoModelForMaskGeneration()
  • inner
    • ~PretrainedMixin
      • instance
        • .MODEL_CLASS_MAPPINGS : Array.<Map>
        • .BASE_IF_FAIL
      • static
        • .supports(model_type) ⇒ boolean
        • .from_pretrained() : Object.from_pretrained

---

models.AutoModel

Helper class which is used to instantiate pretrained models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

• .AutoModel
  • new AutoModel()
  • .MODEL_CLASS_MAPPINGS : Array.<Map>

---

new AutoModel()

Example

const model = await AutoModel.from_pretrained('Xenova/bert-base-uncased');

---

autoModel.MODEL_CLASS_MAPPINGS : Array.<Map>

Kind: instance property of AutoModel

---

models.AutoModelForSequenceClassification

Helper class which is used to instantiate pretrained sequence classification models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForSequenceClassification()

Example

const model = await AutoModelForSequenceClassification.from_pretrained('Xenova/distilbert-base-uncased-finetuned-sst-2-english');

---

models.AutoModelForTokenClassification

Helper class which is used to instantiate pretrained token classification models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForTokenClassification()

Example

const model = await AutoModelForTokenClassification.from_pretrained('Xenova/distilbert-base-multilingual-cased-ner-hrl');

---

models.AutoModelForSeq2SeqLM

Helper class which is used to instantiate pretrained sequence-to-sequence models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForSeq2SeqLM()

Example

const model = await AutoModelForSeq2SeqLM.from_pretrained('Xenova/t5-small');

---

models.AutoModelForSpeechSeq2Seq

Helper class which is used to instantiate pretrained sequence-to-sequence speech-to-text models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForSpeechSeq2Seq()

Example

const model = await AutoModelForSpeechSeq2Seq.from_pretrained('openai/whisper-tiny.en');

---

models.AutoModelForTextToSpectrogram

Helper class which is used to instantiate pretrained sequence-to-sequence text-to-spectrogram models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForTextToSpectrogram()

Example

const model = await AutoModelForTextToSpectrogram.from_pretrained('microsoft/speecht5_tts');

---

models.AutoModelForTextToWaveform

Helper class which is used to instantiate pretrained text-to-waveform models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForTextToWaveform()

Example

const model = await AutoModelForTextToSpectrogram.from_pretrained('facebook/mms-tts-eng');

---

models.AutoModelForCausalLM

Helper class which is used to instantiate pretrained causal language models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForCausalLM()

Example

const model = await AutoModelForCausalLM.from_pretrained('Xenova/gpt2');

---

models.AutoModelForMaskedLM

Helper class which is used to instantiate pretrained masked language models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForMaskedLM()

Example

const model = await AutoModelForMaskedLM.from_pretrained('Xenova/bert-base-uncased');

---

models.AutoModelForQuestionAnswering

Helper class which is used to instantiate pretrained question answering models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForQuestionAnswering()

Example

const model = await AutoModelForQuestionAnswering.from_pretrained('Xenova/distilbert-base-cased-distilled-squad');

---

models.AutoModelForVision2Seq

Helper class which is used to instantiate pretrained vision-to-sequence models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForVision2Seq()

Example

const model = await AutoModelForVision2Seq.from_pretrained('Xenova/vit-gpt2-image-captioning');

---

models.AutoModelForImageClassification

Helper class which is used to instantiate pretrained image classification models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForImageClassification()

Example

const model = await AutoModelForImageClassification.from_pretrained('Xenova/vit-base-patch16-224');

---

models.AutoModelForImageSegmentation

Helper class which is used to instantiate pretrained image segmentation models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForImageSegmentation()

Example

const model = await AutoModelForImageSegmentation.from_pretrained('Xenova/detr-resnet-50-panoptic');

---

models.AutoModelForSemanticSegmentation

Helper class which is used to instantiate pretrained image segmentation models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForSemanticSegmentation()

Example

const model = await AutoModelForSemanticSegmentation.from_pretrained('nvidia/segformer-b3-finetuned-cityscapes-1024-1024');

---

models.AutoModelForUniversalSegmentation

Helper class which is used to instantiate pretrained universal image segmentation models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForUniversalSegmentation()

Example

const model = await AutoModelForUniversalSegmentation.from_pretrained('hf-internal-testing/tiny-random-MaskFormerForInstanceSegmentation');

---

models.AutoModelForObjectDetection

Helper class which is used to instantiate pretrained object detection models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForObjectDetection()

Example

const model = await AutoModelForObjectDetection.from_pretrained('Xenova/detr-resnet-50');

---

models.AutoModelForMaskGeneration

Helper class which is used to instantiate pretrained mask generation models with the from_pretrained function. The chosen model class is determined by the type specified in the model config.

Kind: static class of models

---

new AutoModelForMaskGeneration()

Example

const model = await AutoModelForMaskGeneration.from_pretrained('Xenova/sam-vit-base');

---

models~PretrainedMixin

Base class of all AutoModels. Contains the from_pretrained function which is used to instantiate pretrained models.

Kind: inner class of models

• ~PretrainedMixin
  • instance
    • .MODEL_CLASS_MAPPINGS : Array.<Map>
    • .BASE_IF_FAIL
  • static
    • .supports(model_type) ⇒ boolean
    • .from_pretrained() : Object.from_pretrained

---

pretrainedMixin.MODEL_CLASS_MAPPINGS : Array.<Map>

Mapping from model type to model class.

Kind: instance property of PretrainedMixin

---

pretrainedMixin.BASE_IF_FAIL

Whether to attempt to instantiate the base class (PretrainedModel) if the model type is not found in the mapping.

Kind: instance property of PretrainedMixin

---

PretrainedMixin.supports(model_type) ⇒ boolean

Check whether this AutoModel class supports a given model type.

Kind: static method of PretrainedMixin
Returns: boolean - Whether this class can handle the given model type.

  ParamTypeDescription




model_typestringThe model type from config (e.g., 'bert', 'whisper').

  

---

PretrainedMixin.from_pretrained() : Object.from_pretrained

Kind: static method of PretrainedMixin

---