# JinaEmbeddingsV3

The [Jina-Embeddings-v3](https://huggingface.co/papers/2409.10173) is a multilingual, multi-task text embedding model designed for a variety of NLP applications. Based on the XLM-RoBERTa architecture, this model supports **Rotary Position Embeddings (RoPE)** replacing absolute position embeddings to support long input sequences up to 8192 tokens. Additionally, it features 5 built-in **Task-Specific LoRA Adapters:** that allow the model to generate task-specific embeddings (e.g., for retrieval vs. classification) without increasing inference latency significantly.

You can find the original Jina Embeddings v3 checkpoints under the [Jina AI](https://huggingface.co/jinaai) organization.

> [!TIP]
> Click on the Jina Embeddings v3 models in the right sidebar for more examples of how to apply the model to different language tasks.

The example below demonstrates how to extract features (embeddings) with [Pipeline](/docs/transformers/v5.6.1/en/main_classes/pipelines#transformers.Pipeline), [AutoModel](/docs/transformers/v5.6.1/en/model_doc/auto#transformers.AutoModel), and from the command line.

```py
import torch
from transformers import pipeline

pipeline = pipeline(
    task="feature-extraction",
    model="jinaai/jina-embeddings-v3-hf",
)
# Returns a list of lists containing the embeddings for each token
embeddings = pipeline("Jina Embeddings V3 is great for semantic search.")
```

```py
import torch
from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3-hf")
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3-hf", device_map="auto")

prompt = "Jina Embeddings V3 is great for semantic search."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model(**inputs)
    # The base AutoModel returns the raw hidden states for all tokens
    last_hidden_states = outputs.last_hidden_state

print(f"Features shape: {last_hidden_states.shape}")
```

## Task-Specific LoRA Adapters

A key feature of `JinaEmbeddingsV3` is it's LoRA adapters, which allow you to tailor the output embeddings to specific useful use cases without the overhead of loading entirely different models.

The following tasks are supported:

* **`retrieval.query`**: Used for query embeddings in asymmetric retrieval tasks (e.g., search queries).
* **`retrieval.passage`**: Used for passage embeddings in asymmetric retrieval tasks (e.g., the documents being searched).
* **`separation`**: Used for embeddings in clustering and re-ranking applications.
* **`classification`**: Used for embeddings in classification tasks.
* **`text-matching`**: Used for embeddings in tasks that quantify similarity between two texts, such as Semantic Textual Similarity (STS) or symmetric retrieval tasks.

To generate high-quality sentence or paragraph embeddings, you need to apply **mean pooling** to the model's token embeddings. Mean pooling takes all token embeddings from the model's output and averages them, masking out the padding tokens.

Here is how you can generate sentence embeddings tailored for a retrieval query task using the `AutoModel` API.

```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel

def mean_pooling(model_output, attention_mask):
    # First element of model_output contains all token embeddings
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()

    # Sum the embeddings and divide by the number of non-padding tokens
    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
    return sum_embeddings / sum_mask

sentences = [
    "How is the weather today?", 
    "What is the current weather like today?"
]

tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3-hf")
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3-hf")

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt").to(model.device)

# Set up the adapter mask for your specific task
task = 'retrieval_query'  # Can be any of (retrieval_passage, separation, classification, text_matching) depending on the use-case.

model.load_adapter("jinaai/jina-embeddings-v3-hf", adapter_name=task, adapter_kwargs={"subfolder": task})

model.set_adapter(task)

with torch.no_grad():
    model_output = model(**encoded_input)

embeddings = mean_pooling(model_output, encoded_input["attention_mask"])
embeddings = F.normalize(embeddings, p=2, dim=1)

print(embeddings.shape)
# Output: torch.Size([2, 1024])
```

## JinaEmbeddingsV3Config [[transformers.JinaEmbeddingsV3Config]]

#### transformers.JinaEmbeddingsV3Config[[transformers.JinaEmbeddingsV3Config]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/models/jina_embeddings_v3/configuration_jina_embeddings_v3.py#L31)

This is the configuration class to store the configuration of a JinaEmbeddingsV3Model. It is used to instantiate a Jina Embeddings V3
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the [jinaai/jina-embeddings-v3-hf](https://huggingface.co/jinaai/jina-embeddings-v3-hf)

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.6.1/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/v5.6.1/en/main_classes/configuration#transformers.PreTrainedConfig) for more information.

Examples:

```python
>>> from transformers import JinaEmbeddingsV3Config, JinaEmbeddingsV3Model

>>> # Initializing a Jina-Embeddings-V3 jinaai/jina-embeddings-v3-hf style configuration
>>> configuration = JinaEmbeddingsV3Config()

>>> # Initializing a model (with random weights) from the jinaai/jina-embeddings-v3-hf style configuration
>>> model = JinaEmbeddingsV3Model(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

**Parameters:**

vocab_size (`int`, *optional*, defaults to `250002`) : Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids`.

hidden_size (`int`, *optional*, defaults to `1024`) : Dimension of the hidden representations.

num_hidden_layers (`int`, *optional*, defaults to `24`) : Number of hidden layers in the Transformer decoder.

num_attention_heads (`int`, *optional*, defaults to `16`) : Number of attention heads for each attention layer in the Transformer decoder.

intermediate_size (`int`, *optional*, defaults to `4096`) : Dimension of the MLP representations.

hidden_act (`str`, *optional*, defaults to `gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc.

hidden_dropout_prob (`Union[float, int]`, *optional*, defaults to `0.1`) : The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

attention_probs_dropout_prob (`Union[float, int]`, *optional*, defaults to `0.1`) : The dropout ratio for the attention probabilities.

max_position_embeddings (`int`, *optional*, defaults to `8194`) : The maximum sequence length that this model might ever be used with.

type_vocab_size (`int`, *optional*, defaults to `1`) : The vocabulary size of the `token_type_ids`.

initializer_range (`float`, *optional*, defaults to `0.02`) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

layer_norm_eps (`float`, *optional*, defaults to `1e-05`) : The epsilon used by the layer normalization layers.

pad_token_id (`int`, *optional*, defaults to `1`) : Token id used for padding in the vocabulary.

bos_token_id (`int`, *optional*, defaults to `0`) : Token id used for beginning-of-stream in the vocabulary.

eos_token_id (`Union[int, list[int]]`, *optional*, defaults to `2`) : Token id used for end-of-stream in the vocabulary.

use_cache (`bool`, *optional*, defaults to `True`) : Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True` or when the model is a decoder-only generative model.

classifier_dropout (`Union[float, int]`, *optional*) : The dropout ratio for classifier.

tie_word_embeddings (`bool`, *optional*, defaults to `True`) : Whether to tie weight embeddings according to model's `tied_weights_keys` mapping.

rope_parameters (`Union[~modeling_rope_utils.RopeParameters, dict]`, *optional*) : Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`.

## JinaEmbeddingsV3Model[[transformers.JinaEmbeddingsV3Model]]

#### transformers.JinaEmbeddingsV3Model[[transformers.JinaEmbeddingsV3Model]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/models/jina_embeddings_v3/modeling_jina_embeddings_v3.py#L370)

The bare Jina Embeddings V3 Model outputting raw hidden-states without any specific head on top.

This model inherits from [PreTrainedModel](/docs/transformers/v5.6.1/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.JinaEmbeddingsV3Model.forwardhttps://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/models/jina_embeddings_v3/modeling_jina_embeddings_v3.py#L397[{"name": "input_ids", "val": ": torch.Tensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "token_type_ids", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.Tensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.Tensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.6.1/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.6.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.6.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **token_type_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **inputs_embeds** (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.0[BaseModelOutputWithPooling](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([JinaEmbeddingsV3Config](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3Config)) and inputs.
The [JinaEmbeddingsV3Model](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3Model) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

**Parameters:**

config ([JinaEmbeddingsV3Config](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3Config)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.6.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

add_pooling_layer (`bool`, *optional*, defaults to `True`) : Whether to add a pooling layer

**Returns:**

`[BaseModelOutputWithPooling](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPooling](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([JinaEmbeddingsV3Config](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3Config)) and inputs.

## JinaEmbeddingsV3ForMaskedLM [[transformers.JinaEmbeddingsV3ForMaskedLM]]

#### transformers.JinaEmbeddingsV3ForMaskedLM[[transformers.JinaEmbeddingsV3ForMaskedLM]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/models/jina_embeddings_v3/modeling_jina_embeddings_v3.py#L478)

The Jina Embeddings V3 Model with a `language modeling` head on top."

This model inherits from [PreTrainedModel](/docs/transformers/v5.6.1/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.JinaEmbeddingsV3ForMaskedLM.forwardhttps://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/models/jina_embeddings_v3/modeling_jina_embeddings_v3.py#L499[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "token_type_ids", "val": ": torch.LongTensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.6.1/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.6.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.6.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.
  This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value
  >= 2. All the value in this tensor should be always 0[MaskedLMOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput) or `tuple(torch.FloatTensor)`A [MaskedLMOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([JinaEmbeddingsV3Config](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3Config)) and inputs.
The [JinaEmbeddingsV3ForMaskedLM](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3ForMaskedLM) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Masked language modeling (MLM) loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

Example:

```python
>>> from transformers import AutoTokenizer, JinaEmbeddingsV3ForMaskedLM
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3-hf")
>>> model = JinaEmbeddingsV3ForMaskedLM.from_pretrained("jinaai/jina-embeddings-v3-hf")

>>> inputs = tokenizer("The capital of France is .", return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> # retrieve index of 
>>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]

>>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
>>> tokenizer.decode(predicted_token_id)
...

>>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
>>> # mask labels of non- tokens
>>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)

>>> outputs = model(**inputs, labels=labels)
>>> round(outputs.loss.item(), 2)
...
```

**Parameters:**

config ([JinaEmbeddingsV3ForMaskedLM](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3ForMaskedLM)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.6.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

`[MaskedLMOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput) or `tuple(torch.FloatTensor)``

A [MaskedLMOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([JinaEmbeddingsV3Config](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3Config)) and inputs.

## JinaEmbeddingsV3ForSequenceClassification[[transformers.JinaEmbeddingsV3ForSequenceClassification]]

#### transformers.JinaEmbeddingsV3ForSequenceClassification[[transformers.JinaEmbeddingsV3ForSequenceClassification]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/models/jina_embeddings_v3/modeling_jina_embeddings_v3.py#L582)

XLM-RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.

This model inherits from [PreTrainedModel](/docs/transformers/v5.6.1/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.JinaEmbeddingsV3ForSequenceClassification.forwardhttps://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/models/jina_embeddings_v3/modeling_jina_embeddings_v3.py#L594[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "token_type_ids", "val": ": torch.LongTensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.6.1/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.6.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.6.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.
  This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value
  >= 2. All the value in this tensor should be always  1` a classification loss is computed (Cross-Entropy).0[SequenceClassifierOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)`A [SequenceClassifierOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([JinaEmbeddingsV3Config](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3Config)) and inputs.
The [JinaEmbeddingsV3ForSequenceClassification](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3ForSequenceClassification) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Classification (or regression if config.num_labels==1) loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) -- Classification (or regression if config.num_labels==1) scores (before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

Example of single-label classification:

```python
>>> import torch
>>> from transformers import AutoTokenizer, JinaEmbeddingsV3ForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3-hf")
>>> model = JinaEmbeddingsV3ForSequenceClassification.from_pretrained("jinaai/jina-embeddings-v3-hf")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
...

>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = JinaEmbeddingsV3ForSequenceClassification.from_pretrained("jinaai/jina-embeddings-v3-hf", num_labels=num_labels)

>>> labels = torch.tensor([1])
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
...
```

Example of multi-label classification:

```python
>>> import torch
>>> from transformers import AutoTokenizer, JinaEmbeddingsV3ForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3-hf")
>>> model = JinaEmbeddingsV3ForSequenceClassification.from_pretrained("jinaai/jina-embeddings-v3-hf", problem_type="multi_label_classification")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]

>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = JinaEmbeddingsV3ForSequenceClassification.from_pretrained(
...     "jinaai/jina-embeddings-v3-hf", num_labels=num_labels, problem_type="multi_label_classification"
... )

>>> labels = torch.sum(
...     torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
... ).to(torch.float)
>>> loss = model(**inputs, labels=labels).loss
```

**Parameters:**

config ([JinaEmbeddingsV3ForSequenceClassification](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3ForSequenceClassification)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.6.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

`[SequenceClassifierOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)``

A [SequenceClassifierOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([JinaEmbeddingsV3Config](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3Config)) and inputs.

## JinaEmbeddingsV3ForTokenClassification[[transformers.JinaEmbeddingsV3ForTokenClassification]]

#### transformers.JinaEmbeddingsV3ForTokenClassification[[transformers.JinaEmbeddingsV3ForTokenClassification]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/models/jina_embeddings_v3/modeling_jina_embeddings_v3.py#L667)

The Jina Embeddings V3 transformer with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.

This model inherits from [PreTrainedModel](/docs/transformers/v5.6.1/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.JinaEmbeddingsV3ForTokenClassification.forwardhttps://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/models/jina_embeddings_v3/modeling_jina_embeddings_v3.py#L682[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "token_type_ids", "val": ": torch.LongTensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.6.1/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.6.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.6.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.
  This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value
  >= 2. All the value in this tensor should be always 0[TokenClassifierOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or `tuple(torch.FloatTensor)`A [TokenClassifierOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([JinaEmbeddingsV3Config](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3Config)) and inputs.
The [JinaEmbeddingsV3ForTokenClassification](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3ForTokenClassification) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Classification loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`) -- Classification scores (before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

Example:

```python
>>> from transformers import AutoTokenizer, JinaEmbeddingsV3ForTokenClassification
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3-hf")
>>> model = JinaEmbeddingsV3ForTokenClassification.from_pretrained("jinaai/jina-embeddings-v3-hf")

>>> inputs = tokenizer(
...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
... )

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_token_class_ids = logits.argmax(-1)

>>> # Note that tokens are classified rather then input words which means that
>>> # there might be more predicted token classes than words.
>>> # Multiple token classes might account for the same word
>>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
>>> predicted_tokens_classes
...

>>> labels = predicted_token_class_ids
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
...
```

**Parameters:**

config ([JinaEmbeddingsV3ForTokenClassification](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3ForTokenClassification)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.6.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

`[TokenClassifierOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or `tuple(torch.FloatTensor)``

A [TokenClassifierOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([JinaEmbeddingsV3Config](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3Config)) and inputs.

## JinaEmbeddingsV3ForQuestionAnswering[[transformers.JinaEmbeddingsV3ForQuestionAnswering]]

#### transformers.JinaEmbeddingsV3ForQuestionAnswering[[transformers.JinaEmbeddingsV3ForQuestionAnswering]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/models/jina_embeddings_v3/modeling_jina_embeddings_v3.py#L738)

The Jina Embeddings V3 transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).

This model inherits from [PreTrainedModel](/docs/transformers/v5.6.1/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.JinaEmbeddingsV3ForQuestionAnswering.forwardhttps://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/models/jina_embeddings_v3/modeling_jina_embeddings_v3.py#L749[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "token_type_ids", "val": ": torch.LongTensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "start_positions", "val": ": torch.LongTensor | None = None"}, {"name": "end_positions", "val": ": torch.LongTensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.6.1/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.6.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.6.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.
  This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value
  >= 2. All the value in this tensor should be always 0[QuestionAnsweringModelOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput) or `tuple(torch.FloatTensor)`A [QuestionAnsweringModelOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([JinaEmbeddingsV3Config](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3Config)) and inputs.
The [JinaEmbeddingsV3ForQuestionAnswering](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3ForQuestionAnswering) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
- **start_logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) -- Span-start scores (before SoftMax).
- **end_logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) -- Span-end scores (before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

Example:

```python
>>> from transformers import AutoTokenizer, JinaEmbeddingsV3ForQuestionAnswering
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3-hf")
>>> model = JinaEmbeddingsV3ForQuestionAnswering.from_pretrained("jinaai/jina-embeddings-v3-hf")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()

>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)
...

>>> # target is "nice puppet"
>>> target_start_index = torch.tensor([14])
>>> target_end_index = torch.tensor([15])

>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = outputs.loss
>>> round(loss.item(), 2)
...
```

**Parameters:**

config ([JinaEmbeddingsV3ForQuestionAnswering](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3ForQuestionAnswering)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.6.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

`[QuestionAnsweringModelOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput) or `tuple(torch.FloatTensor)``

A [QuestionAnsweringModelOutput](/docs/transformers/v5.6.1/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([JinaEmbeddingsV3Config](/docs/transformers/v5.6.1/en/model_doc/jina_embeddings_v3#transformers.JinaEmbeddingsV3Config)) and inputs.