Update README.md

README.md

@@ -1,376 +1,2 @@
----
-language: []
-library_name: sentence-transformers
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- generated_from_trainer
-- dataset_size:2054
-- loss:ContrastiveLoss
-base_model: intfloat/multilingual-e5-large
-datasets: []
-widget:
-- source_sentence: 2021 parliament elections
-  sentences:
-  - Wolaita people
-  - 2023 General Elections
-  - government forces
-- source_sentence: Atiku Abubakar
-  sentences:
-  - president of Nigeria
-  - military juntas
-  - Federal Government
-- source_sentence: Election
-  sentences:
-  - Tomorrow's election in Nigeria
-  - Election
-  - Public hospital
-- source_sentence: Ethiopian government
-  sentences:
-  - President E.D Mnangagwa
-  - Sanction on Ethiopia
-  - Dino Melaye
-- source_sentence: International community
-  sentences:
-  - Real-time vote tallying technology
-  - Western interference in Ethiopia
-  - International community
-pipeline_tag: sentence-similarity
----
-
-# SentenceTransformer based on intfloat/multilingual-e5-large
-
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large). It maps sentences & paragraphs to a 1024-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:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb -->
-- **Maximum Sequence Length:** 512 tokens
-- **Output Dimensionality:** 1024 tokens
-- **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **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): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
-  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-  (2): Normalize()
-)
-```
-
-## 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 = [
-    'International community',
-    'International community',
-    'Western interference in Ethiopia',
-]
-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.*
--->
-
-<!--
-## 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: 2,054 training samples
-* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence1                                                                        | sentence2                                                                        | label                                           |
-  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
-  | type    | string                                                                           | string                                                                           | int                                             |
-  | details | <ul><li>min: 3 tokens</li><li>mean: 5.74 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.86 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>0: ~60.20%</li><li>1: ~39.80%</li></ul> |
-* Samples:
-  | sentence1                                      | sentence2                           | label          |
-  |:-----------------------------------------------|:------------------------------------|:---------------|
-  | <code>Nigerian people</code>                   | <code>Nigerian people</code>        | <code>1</code> |
-  | <code>EFF</code>                               | <code>EFF</code>                    | <code>1</code> |
-  | <code>Public strikes and demonstrations</code> | <code>violent demonstrations</code> | <code>0</code> |
-* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
-  ```json
-  {
-      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
-      "margin": 0.5,
-      "size_average": true
-  }
-  ```
-
-### Evaluation Dataset
-
-#### Unnamed Dataset
-
-
-* Size: 514 evaluation samples
-* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence1                                                                        | sentence2                                                                        | label                                           |
-  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
-  | type    | string                                                                           | string                                                                           | int                                             |
-  | details | <ul><li>min: 3 tokens</li><li>mean: 5.74 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.93 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>0: ~59.92%</li><li>1: ~40.08%</li></ul> |
-* Samples:
-  | sentence1                                 | sentence2                                 | label          |
-  |:------------------------------------------|:------------------------------------------|:---------------|
-  | <code>military base in Namibia</code>     | <code>US military bases in Africa</code>  | <code>0</code> |
-  | <code>Putin's authoritarian regime</code> | <code>Putin's authoritarian regime</code> | <code>1</code> |
-  | <code>Election Day</code>                 | <code>Elections</code>                    | <code>1</code> |
-* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
-  ```json
-  {
-      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
-      "margin": 0.5,
-      "size_average": true
-  }
-  ```
-
-### Training Hyperparameters
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: no
-- `prediction_loss_only`: True
-- `per_device_train_batch_size`: 8
-- `per_device_eval_batch_size`: 8
-- `per_gpu_train_batch_size`: None
-- `per_gpu_eval_batch_size`: None
-- `gradient_accumulation_steps`: 1
-- `eval_accumulation_steps`: None
-- `learning_rate`: 5e-05
-- `weight_decay`: 0.0
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1.0
-- `num_train_epochs`: 3.0
-- `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`: False
-- `hub_always_push`: False
-- `gradient_checkpointing`: False
-- `gradient_checkpointing_kwargs`: None
-- `include_inputs_for_metrics`: False
-- `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
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: proportional
-
-</details>
-
-### Training Logs
-| Epoch  | Step | Training Loss |
-|:------:|:----:|:-------------:|
-| 1.9455 | 500  | 0.0161        |
-
-
-### Framework Versions
-- Python: 3.10.13
-- Sentence Transformers: 3.0.1
-- Transformers: 4.41.2
-- PyTorch: 2.1.2
-- Accelerate: 0.30.1
-- Datasets: 2.19.2
-- Tokenizers: 0.19.1
-
-## 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",
-}
-```
-
-#### ContrastiveLoss
-```bibtex
-@inproceedings{hadsell2006dimensionality,
-    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
-    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, 
-    title={Dimensionality Reduction by Learning an Invariant Mapping}, 
-    year={2006},
-    volume={2},
-    number={},
-    pages={1735-1742},
-    doi={10.1109/CVPR.2006.100}
-}
-```
-
-<!--
-## 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.*
--->
+# NED model
+Mulitilingual E5 model designed for NED