Upload model

README.md

@@ -0,0 +1,199 @@
+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

@@ -0,0 +1,181 @@
+{
+  "architectures": [
+    "CustomCLIPModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_custom_clip.CustomCLIPConfig",
+    "AutoModel": "modeling_custom_clip.CustomCLIPModel"
+  },
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.659260036932778,
+  "model_type": "custom-clip-model",
+  "projection_dim": 1024,
+  "text_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_probs_dropout_prob": 0.1,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": 1,
+    "chunk_size_feed_forward": 0,
+    "classifier_dropout": null,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-12,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 512,
+    "min_length": 0,
+    "model_type": "bert",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 12,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 24,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 3,
+    "position_embedding_type": "absolute",
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "type_vocab_size": 2,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "vocab_size": 32000
+  },
+  "text_model_pooler": "CustomCLIPPooler",
+  "text_model_pooler_kwargs": {},
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.2",
+  "vision_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "gelu",
+    "hidden_size": 1280,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "image_size": 224,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 5120,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "clip_vision_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_channels": 3,
+    "num_hidden_layers": 32,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "patch_size": 14,
+    "prefix": null,
+    "problem_type": null,
+    "projection_dim": 1024,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "typical_p": 1.0,
+    "use_bfloat16": false
+  },
+  "vision_config_dict": {
+    "hidden_act": "gelu",
+    "hidden_size": 1280,
+    "intermediate_size": 5120,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 32,
+    "patch_size": 14,
+    "projection_dim": 1024
+  }
+}

configuration_custom_clip.py

@@ -0,0 +1,88 @@
+from copy import deepcopy
+from typing import Optional
+
+import torch
+from transformers import AutoConfig, VisionTextDualEncoderConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class CustomCLIPPooler(torch.nn.Module):
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        first_token_tensor = hidden_states[:, 0, :]
+        return first_token_tensor
+
+
+def get_text_model_pooler(text_model_pooler: str) -> torch.nn.Module:
+    if text_model_pooler == "CustomCLIPPooler":
+        return CustomCLIPPooler
+    else:
+        raise ValueError(f"Unrecognized text model pooler type {text_model_pooler!r}.")
+
+
+def is_valid_text_model_pooler(
+    text_model_pooler: str, suppress_error: bool = False
+) -> bool:
+    try:
+        get_text_model_pooler(text_model_pooler)
+    except ValueError:
+        if not suppress_error:
+            raise
+        return False
+    else:
+        return True
+
+
+class CustomCLIPConfig(VisionTextDualEncoderConfig):
+    model_type = "custom-clip-model"
+
+    DEFAULT_TEXT_MODEL_POOLER_STR: str = "CustomCLIPPooler"
+    DEFAULT_TEXT_MODEL_POOLER_KWARGS: dict = {}
+
+    def __init__(
+        self,
+        *args,
+        text_model_pooler: Optional[str] = None,
+        text_model_pooler_kwargs: Optional[dict] = None,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+
+        self.text_model_pooler = (
+            self.DEFAULT_TEXT_MODEL_POOLER_STR
+            if text_model_pooler is None
+            else text_model_pooler
+        )
+        is_valid_text_model_pooler(self.text_model_pooler, suppress_error=False)
+
+        self.text_model_pooler_kwargs = (
+            self.DEFAULT_TEXT_MODEL_POOLER_KWARGS
+            if text_model_pooler_kwargs is None
+            else text_model_pooler_kwargs
+        )
+
+    @classmethod
+    def from_base(cls, obj: VisionTextDualEncoderConfig):
+        if not isinstance(obj, cls):
+            base = VisionTextDualEncoderConfig
+            if not isinstance(obj, base):
+                raise TypeError(f"obj must be of type {cls!r} or {base!r}.")
+            obj = deepcopy(obj)
+            logger.warning(f"Changing config class from {obj.__class__!r} to {cls!r}.")
+            obj.__class__ = cls
+
+            def setattr_with_warning(object, name, value):
+                logger.warning(f"Setting {name!r} to {value!r}.")
+                setattr(object, name, value)
+
+            setattr_with_warning(
+                obj, "text_model_pooler", cls.DEFAULT_TEXT_MODEL_POOLER_STR
+            )
+            setattr_with_warning(
+                obj, "text_model_pooler_kwargs", cls.DEFAULT_TEXT_MODEL_POOLER_KWARGS
+            )
+        return obj
+
+
+AutoConfig.register(CustomCLIPConfig.model_type, CustomCLIPConfig)

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:423a03a2873e4eda3ff4ee2b99f0e34dccbdd9f5a4a8996219d7bc0aadb9283b
+size 3311897444

modeling_custom_clip.py

@@ -0,0 +1,45 @@
+"""
+Subclasses VisionTextDualEncoderModel to customize text pooler.
+"""
+
+from typing import Optional
+
+import torch
+from transformers import AutoModel, VisionTextDualEncoderModel
+
+from .configuration_custom_clip import CustomCLIPConfig, get_text_model_pooler
+
+
+# @add_start_docstrings(CUSTOM_CLIP_START_DOCSTRING)
+class CustomCLIPModel(VisionTextDualEncoderModel):
+    config_class = CustomCLIPConfig
+
+    DEFAULT_TEXT_MODEL_POOLER_TYPE: torch.nn.Module = get_text_model_pooler(
+        CustomCLIPConfig.DEFAULT_TEXT_MODEL_POOLER_STR
+    )
+    DEFAULT_TEXT_MODEL_POOLER_KWARGS: dict = (
+        CustomCLIPConfig.DEFAULT_TEXT_MODEL_POOLER_KWARGS
+    )
+
+    def __init__(
+        self, config: Optional[CustomCLIPConfig.__base__] = None, *args, **kwargs
+    ):
+        config = config if config is None else CustomCLIPConfig.from_base(config)
+        super().__init__(
+            config,  # surprisingly, `super` is unnecessary, possibly due to implementation of CustomCLIPConfig.__init__?
+            *args,
+            **kwargs,
+        )
+
+        self.text_model.pooler = (
+            (self.DEFAULT_TEXT_MODEL_POOLER_TYPE)(
+                **self.DEFAULT_TEXT_MODEL_POOLER_KWARGS
+            )
+            if config is None
+            else get_text_model_pooler(config.text_model_pooler)(
+                **config.text_model_pooler_kwargs
+            )
+        )
+
+
+AutoModel.register(CustomCLIPConfig, CustomCLIPModel)