Upload folder using huggingface_hub

step23A/1_Pooling/config.json

@@ -0,0 +1,5 @@
+{
+    "embedding_dimension": 768,
+    "pooling_mode": "mean",
+    "include_prompt": true
+}

step23A/README.md

@@ -0,0 +1,423 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:3651
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Mugitangira prologue yigitabo cyanjye, nanditse ko kuvuka ari umukobwa
+    byangiza ubuzima bwawe, kandi kimwe mubibazo bikomeye navuze mubiganiro bitangira
+    nukuvuka ari igitsina gore.
+  sentences:
+  - At the beginning of the prologue of my book, I write that being born female is
+    dangerous to your health, and one of the critical issues that I spoke about in
+    the opening discussion is being born female.
+  - Hari abantu benshi bacumbikiwe muri ibyo byumba.
+  - This is what Sara and Mrs. and Mr. Kwame said
+- source_sentence: Intumwa Pawulo yerekanye ko idini idashingiye ku migenzo n’imihango
+  sentences:
+  - It was a lot of fun to see how all the kids knew Sandrine and am looking forward
+    to doing other adventures with Kingfisher Journeys!
+  - The guide on our bus, Diderero, went out of his way to deliver a wonderful and
+    engaging experience...and the animals joined in on the fun.
+  - The apostle Paul pointed out that religion is not based on tradition and ritual
+- source_sentence: Urakoze Rwandan adventures
+  sentences:
+  - Gorilla Trek Africa (GTA) provided transportation from the Kigali airport to the
+    hotel and back again (we stayed at 5 Volcanos -- more about that later), then
+    to and from the park area where you're dropped off by your GTA guide.
+  - 'Mutamuriza: Thank you for making our work it easier'
+  - Thanks Rwandan adventures!
+- source_sentence: Intego zikomeye zigomba kongeraho kwemeza ko zifite intego zihagije
+    zijyanye n’ikirere kiri munsi ya 2 ° C.
+  sentences:
+  - Muri Amerika y'Epfo hubakwa toni zisaga miriyoni imwe.
+  - Intensity targets need to additionally ensure that they are ambitious enough to
+    be in line with the well-below 2°C climate scenario.
+  - Bavuga ko Hugh Hefner akeneye pilule imeze nk'amadayimoni.
+- source_sentence: Hano hari kugabanuka kubuntu hafi ya bose!
+  sentences:
+  - There are generous discounts for nearly everyone!
+  - Umugabo w'igitsina gore yapfuye mu buryo buteye ubwoba.
+  - You can avoid a flood of information and prevent an information shortfall, which
+    could mean important measures not being implemented in time for deadlines.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: semrel2024 dev
+      type: semrel2024_dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.5267270524066402
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.5311528924947518
+      name: Spearman Cosine
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Supported Modality:** Text
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
+  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
+)
+```
+
+## 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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Hano hari kugabanuka kubuntu hafi ya bose!',
+    'There are generous discounts for nearly everyone!',
+    'You can avoid a flood of information and prevent an information shortfall, which could mean important measures not being implemented in time for deadlines.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.6076, 0.2924],
+#         [0.6076, 1.0000, 0.2560],
+#         [0.2924, 0.2560, 1.0000]])
+```
+<!--
+### 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
+
+#### Semantic Similarity
+
+* Dataset: `semrel2024_dev`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.5267     |
+| **spearman_cosine** | **0.5312** |
+
+<!--
+## 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
+
+#### Unnamed Dataset
+
+* Size: 3,651 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 100 samples:
+  |          | anchor                                                                            | positive                                                                          |
+  |:---------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type     | string                                                                            | string                                                                            |
+  | modality | text                                                                              | text                                                                              |
+  | details  | <ul><li>min: 6 tokens</li><li>mean: 18.33 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 20.91 tokens</li><li>max: 63 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                            | positive                                                                                                                                                                         |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Abantu bagenda, ukeneye ibicuruzwa bidasanzwe, byoroheje cyane hamwe nudushya twikoranabuhanga kandi ntangirira kuriyi myifatire kandi rwose nkunda ubucuruzi bwose.</code> | <code>The people traveling, you need the special product, very light with the technology innovation and I start from this attitude and really I do love all the business.</code> |
+  | <code>mugomba guhora mwishimye</code>                                                                                                                                             | <code>you must always be happy</code>                                                                                                                                            |
+  | <code>Twabonye izindi ntare mu majyaruguru, ntabwo rero zose zabuze.</code>                                                                                                       | <code>And we saw other lions in the northern sector, so not all was lost.</code>                                                                                                 |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 35,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `num_train_epochs`: 2
+- `learning_rate`: 5e-06
+- `warmup_steps`: 0.1
+- `per_device_eval_batch_size`: 16
+- `load_best_model_at_end`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `per_device_train_batch_size`: 16
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `learning_rate`: 5e-06
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_steps`: 0.1
+- `optim`: adamw_torch
+- `optim_args`: None
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `optim_target_modules`: None
+- `gradient_accumulation_steps`: 1
+- `average_tokens_across_devices`: True
+- `max_grad_norm`: 1.0
+- `label_smoothing_factor`: 0.0
+- `bf16`: False
+- `fp16`: False
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `use_cache`: False
+- `neftune_noise_alpha`: None
+- `torch_empty_cache_steps`: None
+- `auto_find_batch_size`: False
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `include_num_input_tokens_seen`: no
+- `log_level`: passive
+- `log_level_replica`: warning
+- `disable_tqdm`: False
+- `project`: huggingface
+- `trackio_space_id`: None
+- `trackio_bucket_id`: None
+- `trackio_static_space_id`: None
+- `per_device_eval_batch_size`: 16
+- `prediction_loss_only`: True
+- `eval_on_start`: False
+- `eval_do_concat_batches`: True
+- `eval_use_gather_object`: False
+- `eval_accumulation_steps`: None
+- `include_for_metrics`: []
+- `batch_eval_metrics`: False
+- `save_only_model`: False
+- `save_on_each_node`: False
+- `enable_jit_checkpoint`: False
+- `push_to_hub`: False
+- `hub_private_repo`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_always_push`: False
+- `hub_revision`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `restore_callback_states_from_checkpoint`: False
+- `full_determinism`: False
+- `seed`: 42
+- `data_seed`: None
+- `use_cpu`: False
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `dataloader_prefetch_factor`: None
+- `remove_unused_columns`: True
+- `label_names`: None
+- `train_sampling_strategy`: random
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `ddp_static_graph`: None
+- `ddp_backend`: None
+- `ddp_timeout`: 1800
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `deepspeed`: None
+- `debug`: []
+- `skip_memory_metrics`: True
+- `do_predict`: False
+- `resume_from_checkpoint`: None
+- `warmup_ratio`: None
+- `local_rank`: -1
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch   | Step    | Training Loss | semrel2024_dev_spearman_cosine |
+|:-------:|:-------:|:-------------:|:------------------------------:|
+| 0.0873  | 20      | 1.9500        | -                              |
+| 0.1747  | 40      | 1.1022        | -                              |
+| 0.2620  | 60      | 0.7373        | -                              |
+| 0.3493  | 80      | 0.5006        | -                              |
+| 0.4367  | 100     | 0.4222        | -                              |
+| 0.5240  | 120     | 0.2813        | -                              |
+| 0.6114  | 140     | 0.2375        | -                              |
+| 0.6987  | 160     | 0.2114        | -                              |
+| 0.7860  | 180     | 0.1936        | -                              |
+| 0.8734  | 200     | 0.1677        | -                              |
+| 0.9607  | 220     | 0.1842        | -                              |
+| **1.0** | **229** | **-**         | **0.5345**                     |
+| 1.0480  | 240     | 0.1033        | -                              |
+| 1.1354  | 260     | 0.1121        | -                              |
+| 1.2227  | 280     | 0.0862        | -                              |
+| 1.3100  | 300     | 0.0753        | -                              |
+| 1.3974  | 320     | 0.1084        | -                              |
+| 1.4847  | 340     | 0.1165        | -                              |
+| 1.5721  | 360     | 0.0686        | -                              |
+| 1.6594  | 380     | 0.1230        | -                              |
+| 1.7467  | 400     | 0.0922        | -                              |
+| 1.8341  | 420     | 0.0809        | -                              |
+| 1.9214  | 440     | 0.1148        | -                              |
+| 2.0     | 458     | -             | 0.5312                         |
+
+* The bold row denotes the saved checkpoint.
+
+### Training Time
+- **Training**: 6.3 hours
+- **Evaluation**: 1.7 minutes
+- **Total**: 6.4 hours
+
+### Framework Versions
+- Python: 3.10.20
+- Sentence Transformers: 5.5.1
+- Transformers: 5.7.0
+- PyTorch: 2.7.1+cu118
+- Accelerate: 1.13.0
+- Datasets: 5.0.0
+- Tokenizers: 0.22.2
+
+## 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{oord2019representationlearningcontrastivepredictive,
+      title={Representation Learning with Contrastive Predictive Coding},
+      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
+      year={2019},
+      eprint={1807.03748},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1807.03748},
+}
+```
+
+<!--
+## 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.*
+-->

step23A/config.json

@@ -0,0 +1,30 @@
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.7.0",
+  "type_vocab_size": 2,
+  "use_cache": false,
+  "vocab_size": 30000
+}

step23A/config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "pytorch": "2.7.1+cu118",
+    "sentence_transformers": "5.5.1",
+    "transformers": "5.7.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "cosine"
+}

step23A/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55002cafa2fe0055e08c0da75d5c933d7e71d87b40a08f7477081034c1bc5839
+size 436347736

step23A/modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  }
+]

step23A/sentence_bert_config.json

@@ -0,0 +1,10 @@
+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

step23A/tokenizer_config.json

@@ -0,0 +1,22 @@
+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": true,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 128,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}