Add new SentenceTransformer model

README.md

@@ -0,0 +1,425 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:12
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/clip-ViT-L-14
+widget:
+- source_sentence: 'the main power cable is connected with LPT '
+  sentences:
+  - 'the main power cable is connected with LPT '
+  - 'the main power cable is connected with LPT '
+  - /content/sample_data/images/LPT (2).jpeg
+- source_sentence: 'the fuse is not blown it is working properly '
+  sentences:
+  - 'the fuse is not blown it is working properly '
+  - 'the fuse is not blown it is working properly '
+  - /content/sample_data/images/LPT (16).jpeg
+- source_sentence: 'the fuse is blown and this might not work properly '
+  sentences:
+  - /content/sample_data/images/LPT (20).jpeg
+  - 'the fuse is blown and this might not work properly '
+  - 'the fuse is blown and this might not work properly '
+- source_sentence: 'the fuse is blown and this might not work properly '
+  sentences:
+  - 'the fuse is blown and this might not work properly '
+  - /content/sample_data/images/LPT (21).jpeg
+  - 'the fuse is blown and this might not work properly '
+- source_sentence: 'the main power cable is not connected with LPT '
+  sentences:
+  - 'the main power cable is not connected with LPT '
+  - /content/sample_data/images/LPT (4).jpeg
+  - 'the main power cable is not connected with LPT '
+datasets:
+- machinev/multimodalLPT2
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+model-index:
+- name: SentenceTransformer based on sentence-transformers/clip-ViT-L-14
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: yt title thumbnail train
+      type: yt-title-thumbnail-train
+    metrics:
+    - type: cosine_accuracy
+      value: 0.0
+      name: Cosine Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: yt title thumbnail validation
+      type: yt-title-thumbnail-validation
+    metrics:
+    - type: cosine_accuracy
+      value: 0.0
+      name: Cosine Accuracy
+---
+
+# SentenceTransformer based on sentence-transformers/clip-ViT-L-14
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/clip-ViT-L-14](https://huggingface.co/sentence-transformers/clip-ViT-L-14) on the [multimodal_lpt2](https://huggingface.co/datasets/machinev/multimodalLPT2) dataset. It maps sentences & paragraphs to a None-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/clip-ViT-L-14](https://huggingface.co/sentence-transformers/clip-ViT-L-14) <!-- at revision 3b12140ad0f9750045e404f187cfccd04bcaf250 -->
+- **Maximum Sequence Length:** None tokens
+- **Output Dimensionality:** None dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [multimodal_lpt2](https://huggingface.co/datasets/machinev/multimodalLPT2)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): CLIPModel()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("machinev/model")
+# Run inference
+sentences = [
+    'the main power cable is not connected with LPT ',
+    '/content/sample_data/images/LPT (4).jpeg',
+    'the main power cable is not connected with LPT ',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Triplet
+
+* Datasets: `yt-title-thumbnail-train` and `yt-title-thumbnail-validation`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | yt-title-thumbnail-train | yt-title-thumbnail-validation |
+|:--------------------|:-------------------------|:------------------------------|
+| **cosine_accuracy** | **0.0**                  | **0.0**                       |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### multimodal_lpt2
+
+* Dataset: [multimodal_lpt2](https://huggingface.co/datasets/machinev/multimodalLPT2) at [9f649f9](https://huggingface.co/datasets/machinev/multimodalLPT2/tree/9f649f9c95cc375b7ec5895fb47f642f251d288e)
+* Size: 12 training samples
+* Columns: <code>text</code>, <code>image_path</code>, <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 12 samples:
+  |         | text                                                                               | image_path                                                                         | anchor                            | positive                                                                           | negative                                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | PIL.JpegImagePlugin.JpegImageFile | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 11 tokens</li><li>mean: 11.42 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 18.42 tokens</li><li>max: 19 tokens</li></ul> | <ul><li></li></ul>                | <ul><li>min: 11 tokens</li><li>mean: 11.42 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 11.42 tokens</li><li>max: 12 tokens</li></ul> |
+* Samples:
+  | text                                                         | image_path                                            | anchor                                                                                           | positive                                                     | negative                                                     |
+  |:-------------------------------------------------------------|:------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------|:-------------------------------------------------------------|
+  | <code>the main power cable is not connected with LPT </code> | <code>/content/sample_data/images/LPT (1).jpeg</code> | <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3024x4032 at 0x7D40680FFFD0></code> | <code>the main power cable is not connected with LPT </code> | <code>the main power cable is not connected with LPT </code> |
+  | <code>the main power cable is connected with LPT </code>     | <code>/content/sample_data/images/LPT (2).jpeg</code> | <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3024x4032 at 0x7D40680FDF90></code> | <code>the main power cable is connected with LPT </code>     | <code>the main power cable is connected with LPT </code>     |
+  | <code>the main power cable is connected with LPT </code>     | <code>/content/sample_data/images/LPT (3).jpeg</code> | <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3024x4032 at 0x7D4063F4C610></code> | <code>the main power cable is connected with LPT </code>     | <code>the main power cable is connected with LPT </code>     |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### multimodal_lpt2
+
+* Dataset: [multimodal_lpt2](https://huggingface.co/datasets/machinev/multimodalLPT2) at [9f649f9](https://huggingface.co/datasets/machinev/multimodalLPT2/tree/9f649f9c95cc375b7ec5895fb47f642f251d288e)
+* Size: 12 evaluation samples
+* Columns: <code>text</code>, <code>image_path</code>, <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 12 samples:
+  |         | text                                                                               | image_path                                                                         | anchor                            | positive                                                                           | negative                                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | PIL.JpegImagePlugin.JpegImageFile | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 11 tokens</li><li>mean: 11.42 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 18.42 tokens</li><li>max: 19 tokens</li></ul> | <ul><li></li></ul>                | <ul><li>min: 11 tokens</li><li>mean: 11.42 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 11.42 tokens</li><li>max: 12 tokens</li></ul> |
+* Samples:
+  | text                                                         | image_path                                            | anchor                                                                                           | positive                                                     | negative                                                     |
+  |:-------------------------------------------------------------|:------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------|:-------------------------------------------------------------|
+  | <code>the main power cable is not connected with LPT </code> | <code>/content/sample_data/images/LPT (1).jpeg</code> | <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3024x4032 at 0x7D4063B84B50></code> | <code>the main power cable is not connected with LPT </code> | <code>the main power cable is not connected with LPT </code> |
+  | <code>the main power cable is connected with LPT </code>     | <code>/content/sample_data/images/LPT (2).jpeg</code> | <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3024x4032 at 0x7D4063F4D190></code> | <code>the main power cable is connected with LPT </code>     | <code>the main power cable is connected with LPT </code>     |
+  | <code>the main power cable is connected with LPT </code>     | <code>/content/sample_data/images/LPT (3).jpeg</code> | <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3024x4032 at 0x7D4063F4C7D0></code> | <code>the main power cable is connected with LPT </code>     | <code>the main power cable is connected with LPT </code>     |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 0.0001
+- `num_train_epochs`: 2
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 0.0001
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch | Step | Training Loss | Validation Loss | yt-title-thumbnail-train_cosine_accuracy | yt-title-thumbnail-validation_cosine_accuracy |
+|:-----:|:----:|:-------------:|:---------------:|:----------------------------------------:|:---------------------------------------------:|
+| -1    | -1   | -             | -               | 0.0                                      | 0.0                                           |
+| 1.0   | 1    | 8.5381        | 7.5693          | -                                        | -                                             |
+| 2.0   | 2    | 7.5693        | 7.1228          | -                                        | -                                             |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.48.3
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.3.2
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "/root/.cache/huggingface/hub/models--sentence-transformers--clip-ViT-L-14/snapshots/3b12140ad0f9750045e404f187cfccd04bcaf250/0_CLIPModel",
+  "architectures": [
+    "CLIPModel"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
+  "projection_dim": 768,
+  "text_config": {
+    "dropout": 0.0,
+    "hidden_size": 768,
+    "intermediate_size": 3072,
+    "model_type": "clip_text_model",
+    "num_attention_heads": 12
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "vision_config": {
+    "dropout": 0.0,
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "model_type": "clip_vision_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "patch_size": 14
+  }
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.48.3",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:90ffdd36f1d515660008a6e288ae6e9b51401ae65daadc06a86215ac360b8269
+size 1710537716

modules.json

@@ -0,0 +1,8 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.CLIPModel"
+  }
+]

preprocessor_config.json

@@ -0,0 +1,28 @@
+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "processor_class": "CLIPProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,32 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "49406": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "49407": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "processor_class": "CLIPProcessor",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}