Testing upload of a test model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,702 @@
+---
+language: []
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:7005
+- loss:MultipleNegativesRankingLoss_with_logging
+base_model: Alibaba-NLP/gte-large-en-v1.5
+datasets: []
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_accuracy@30
+- cosine_accuracy@50
+- cosine_accuracy@100
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_precision@30
+- cosine_precision@50
+- cosine_precision@100
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_recall@30
+- cosine_recall@50
+- cosine_recall@100
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+- dot_accuracy@1
+- dot_accuracy@3
+- dot_accuracy@5
+- dot_accuracy@10
+- dot_accuracy@30
+- dot_accuracy@50
+- dot_accuracy@100
+- dot_precision@1
+- dot_precision@3
+- dot_precision@5
+- dot_precision@10
+- dot_precision@30
+- dot_precision@50
+- dot_precision@100
+- dot_recall@1
+- dot_recall@3
+- dot_recall@5
+- dot_recall@10
+- dot_recall@30
+- dot_recall@50
+- dot_recall@100
+- dot_ndcg@10
+- dot_mrr@10
+- dot_map@100
+widget:
+- source_sentence: What are the client's target industries?
+  sentences:
+  - 'Right.
+
+    And also, you know, heavy equipment.
+
+    Okay, I understand.'
+  - 'And there''s a full spectrum.
+
+    It''s all about your order offering.
+
+    Right.
+
+    If you''re offering, like, a full design platform where now we have way more engagement
+    in terms of employee being able to get it from one place, and that could be.
+
+    That could take away again, like, my pitch would be basically being on the show.'
+  - 'Our competitors are billion dollar corporations.
+
+    So Experian Epsilon, which is owned by IPG or publicis, big french company, Axiom,
+    which is owned by IPG.
+
+    Inter public group, huge agency.
+
+    So it''s nice competing against multibillion dollar corporations because they
+    move at the speed of the Statue of Liberty.'
+- source_sentence: What is the strategy for heating products?
+  sentences:
+  - 'Then when you go in to take a look, you say, okay, I''ve got this.
+
+    Now I need to record my test results so that we do down here.
+
+    And we say, okay, this is me, so I''ll pick myself.
+
+    And here we go.
+
+    So once you''re in here, you say, okay, it''s inspector me.'
+  - 'I don''t think we make any margin on these products.
+
+    I''m going to put it on here, though, because I want to add different ones.
+
+    So three in one and then.
+
+    Underfloor heating?'
+  - 'How are others using it?
+
+    Use cases like.
+
+    Yeah, for example, we have one, one partner, there''s climbo.'
+- source_sentence: What feature did Aseel request regarding budget information display?
+  sentences:
+  - 'So you want to do your west coast.
+
+    Do you want to do 10:00 a.m.
+
+    on the morning of 13th?'
+  - 'But the only thing that I just was thinking about is, for example, if I was a
+    head teacher and I''m about to approve an order and obviously I go and click on
+    the three dots and it tells me my geo budget department by GL budget and obviously
+    tells you what your total budget is, your spend and what''s remaining.
+
+    Is there a way in which I can see what actually went under proof expenditure?
+
+    So it should be.
+
+    So to see how much has been committed against the budget?'
+  - 'Awesome.
+
+    And speaking of releases, is there any way I''m not getting the.
+
+    And I''m sure Chris probably is.'
+- source_sentence: Does the customer have any other EAP-like resources available?
+  sentences:
+  - 'Every time I make a post, I get.
+
+    I get just a ton of inquiries, you know?
+
+    And we''re just.
+
+    We''re doing a bunch of cool operational stuff right now, so we''re just trying
+    to get that all figured out, you know?
+
+    Yeah.
+
+    Well, hey, let me give you a rundown of a couple things I''m doing with, like,
+    people in your kind of peripheral.
+
+    Just so you know what we''re trying to do to boost the voices of you and agencies
+    like you.'
+  - 'So we need Kim and Manju.
+
+    We need to account that for production downtime for on 16th.
+
+    No cutover plan.'
+  - 'They''re thinking, well, there we have them already, and they offer all these
+    things.
+
+    This is pretty great, you know, because we also use, so we have Voya life insurance,
+    and through Voya, they offer a couple eap type of resources, too.
+
+    Right.
+
+    So we have additional assistance with another program.
+
+    Right.
+
+    But with our eap, which is through Magellan, they would just usually would just
+    be better than the other comparisons when it came down to it.'
+- source_sentence: What was Nathan's response to the initial proposal from Global
+    Air U?
+  sentences:
+  - But I was listening to everything that you were talking about.
+  - 'And hopefully that should update now in your account in a second.
+
+    Yeah.
+
+    If you give that a go now, you should see all the way to August 2025.'
+  - 'I don''t see on the proposal.
+
+    I don''t see anything class or the class related.
+
+    Um.
+
+    Oh, so for the course.
+
+    No, no.'
+pipeline_tag: sentence-similarity
+model-index:
+- name: SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.32793959007551243
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.48975188781014023
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5663430420711975
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.6612729234088457
+      name: Cosine Accuracy@10
+    - type: cosine_accuracy@30
+      value: 0.7669902912621359
+      name: Cosine Accuracy@30
+    - type: cosine_accuracy@50
+      value: 0.8155339805825242
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.8597626752966558
+      name: Cosine Accuracy@100
+    - type: cosine_precision@1
+      value: 0.32793959007551243
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.1902193455591514
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.13829557713052856
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08716289104638619
+      name: Cosine Precision@10
+    - type: cosine_precision@30
+      value: 0.038439410284070476
+      name: Cosine Precision@30
+    - type: cosine_precision@50
+      value: 0.025717367853290186
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.014282632146709814
+      name: Cosine Precision@100
+    - type: cosine_recall@1
+      value: 0.19877399359600004
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.32606462218112703
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.39100529100529097
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.475571479940412
+      name: Cosine Recall@10
+    - type: cosine_recall@30
+      value: 0.6031369325867708
+      name: Cosine Recall@30
+    - type: cosine_recall@50
+      value: 0.660217290799815
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.7195099398982894
+      name: Cosine Recall@100
+    - type: cosine_ndcg@10
+      value: 0.3784769275629581
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.42950420369514186
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.3193224907975288
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.3290183387270766
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.4886731391585761
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.5717367853290184
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.6634304207119741
+      name: Dot Accuracy@10
+    - type: dot_accuracy@30
+      value: 0.7669902912621359
+      name: Dot Accuracy@30
+    - type: dot_accuracy@50
+      value: 0.8133764832793959
+      name: Dot Accuracy@50
+    - type: dot_accuracy@100
+      value: 0.8619201725997843
+      name: Dot Accuracy@100
+    - type: dot_precision@1
+      value: 0.3290183387270766
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.18985976267529667
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.1387270765911543
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.08737864077669903
+      name: Dot Precision@10
+    - type: dot_precision@30
+      value: 0.038511326860841424
+      name: Dot Precision@30
+    - type: dot_precision@50
+      value: 0.025652642934196335
+      name: Dot Precision@50
+    - type: dot_precision@100
+      value: 0.0143042071197411
+      name: Dot Precision@100
+    - type: dot_recall@1
+      value: 0.19940326364274585
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.32588483073919966
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.39370216263420144
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.4770997071967946
+      name: Dot Recall@10
+    - type: dot_recall@30
+      value: 0.6043595143918767
+      name: Dot Recall@30
+    - type: dot_recall@50
+      value: 0.659138542148251
+      name: Dot Recall@50
+    - type: dot_recall@100
+      value: 0.7219987671443983
+      name: Dot Recall@100
+    - type: dot_ndcg@10
+      value: 0.3791495475200093
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.4305302991387128
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.31951258454174397
+      name: Dot Map@100
+---
+
+# SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5). 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:** [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) <!-- at revision 104333d6af6f97649377c2afbde10a7704870c7b -->
+- **Maximum Sequence Length:** 8192 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': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, '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("model_3")
+# Run inference
+sentences = [
+    "What was Nathan's response to the initial proposal from Global Air U?",
+    "I don't see on the proposal.\nI don't see anything class or the class related.\nUm.\nOh, so for the course.\nNo, no.",
+    'And hopefully that should update now in your account in a second.\nYeah.\nIf you give that a go now, you should see all the way to August 2025.',
+]
+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
+
+#### Information Retrieval
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric               | Value      |
+|:---------------------|:-----------|
+| cosine_accuracy@1    | 0.3279     |
+| cosine_accuracy@3    | 0.4898     |
+| cosine_accuracy@5    | 0.5663     |
+| cosine_accuracy@10   | 0.6613     |
+| cosine_accuracy@30   | 0.767      |
+| cosine_accuracy@50   | 0.8155     |
+| cosine_accuracy@100  | 0.8598     |
+| cosine_precision@1   | 0.3279     |
+| cosine_precision@3   | 0.1902     |
+| cosine_precision@5   | 0.1383     |
+| cosine_precision@10  | 0.0872     |
+| cosine_precision@30  | 0.0384     |
+| cosine_precision@50  | 0.0257     |
+| cosine_precision@100 | 0.0143     |
+| cosine_recall@1      | 0.1988     |
+| cosine_recall@3      | 0.3261     |
+| cosine_recall@5      | 0.391      |
+| cosine_recall@10     | 0.4756     |
+| cosine_recall@30     | 0.6031     |
+| cosine_recall@50     | 0.6602     |
+| cosine_recall@100    | 0.7195     |
+| cosine_ndcg@10       | 0.3785     |
+| cosine_mrr@10        | 0.4295     |
+| **cosine_map@100**   | **0.3193** |
+| dot_accuracy@1       | 0.329      |
+| dot_accuracy@3       | 0.4887     |
+| dot_accuracy@5       | 0.5717     |
+| dot_accuracy@10      | 0.6634     |
+| dot_accuracy@30      | 0.767      |
+| dot_accuracy@50      | 0.8134     |
+| dot_accuracy@100     | 0.8619     |
+| dot_precision@1      | 0.329      |
+| dot_precision@3      | 0.1899     |
+| dot_precision@5      | 0.1387     |
+| dot_precision@10     | 0.0874     |
+| dot_precision@30     | 0.0385     |
+| dot_precision@50     | 0.0257     |
+| dot_precision@100    | 0.0143     |
+| dot_recall@1         | 0.1994     |
+| dot_recall@3         | 0.3259     |
+| dot_recall@5         | 0.3937     |
+| dot_recall@10        | 0.4771     |
+| dot_recall@30        | 0.6044     |
+| dot_recall@50        | 0.6591     |
+| dot_recall@100       | 0.722      |
+| dot_ndcg@10          | 0.3791     |
+| dot_mrr@10           | 0.4305     |
+| dot_map@100          | 0.3195     |
+
+<!--
+## 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: 7,005 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                            |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 14.59 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 60.98 tokens</li><li>max: 170 tokens</li></ul> |
+* Samples:
+  | anchor                                                                           | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  |:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What progress has been made with setting up Snowflake share?</code>        | <code>He finally got around to giving me the information necessary to set up Snowflake share.<br>I will be submitting the application to get back set up.<br>Once the database is set up, then we just need to figure out how to configure Snowflake share, which it's going to be in the documentation.<br>We should be set on that end.<br>We also are going to have a conversation with someone named Peter Tsanghen, who's, who owns Jira platform.<br>Great.</code> |
+  | <code>Who is Peter Tsanghen and what is the planned interaction with him?</code> | <code>He finally got around to giving me the information necessary to set up Snowflake share.<br>I will be submitting the application to get back set up.<br>Once the database is set up, then we just need to figure out how to configure Snowflake share, which it's going to be in the documentation.<br>We should be set on that end.<br>We also are going to have a conversation with someone named Peter Tsanghen, who's, who owns Jira platform.<br>Great.</code> |
+  | <code>Who is Peter Tsanghen and what is the planned interaction with him?</code> | <code>Uh, and so now we just have to meet with Peter.<br>Peter is someone who I used to work with on, he used to work on, uh, syndicated data products.<br>So I used to work with him on that.</code>                                                                                                                                                                                                                                                                    |
+* Loss: <code>__main__.MultipleNegativesRankingLoss_with_logging</code>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 4
+- `num_train_epochs`: 2
+- `max_steps`: 1751
+- `disable_tqdm`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 4
+- `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
+- `num_train_epochs`: 2
+- `max_steps`: 1751
+- `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
+- `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`: True
+- `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}
+- `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
+- `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_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | cosine_map@100 |
+|:------:|:----:|:--------------:|
+| 0.0114 | 20   | 0.2538         |
+| 0.0228 | 40   | 0.2601         |
+| 0.0342 | 60   | 0.2724         |
+| 0.0457 | 80   | 0.2911         |
+| 0.0571 | 100  | 0.2976         |
+| 0.0685 | 120  | 0.3075         |
+| 0.0799 | 140  | 0.3071         |
+| 0.0913 | 160  | 0.3111         |
+| 0.1027 | 180  | 0.3193         |
+
+
+### Framework Versions
+- Python: 3.10.9
+- Sentence Transformers: 3.0.1
+- Transformers: 4.39.3
+- PyTorch: 2.3.1+cu121
+- Accelerate: 0.31.0
+- Datasets: 2.20.0
+- Tokenizers: 0.15.2
+
+## 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",
+}
+```
+
+<!--
+## 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 +1,44 @@
+{
+  "_name_or_path": "Alibaba-NLP/gte-large-en-v1.5",
+  "architectures": [
+    "NewModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig",
+    "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
+    "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
+    "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
+    "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
+    "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
+    "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
+  },
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "layer_norm_type": "layer_norm",
+  "logn_attention_clip1": false,
+  "logn_attention_scale": false,
+  "max_position_embeddings": 8192,
+  "model_type": "new",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pack_qkv": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "rope",
+  "rope_scaling": {
+    "factor": 2.0,
+    "type": "ntk"
+  },
+  "rope_theta": 160000,
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.3",
+  "type_vocab_size": 2,
+  "unpad_inputs": false,
+  "use_memory_efficient_attention": false,
+  "vocab_size": 30528
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.39.3",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+size 1736585680

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 8192,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 8000,
+  "model_max_length": 8192,
+  "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]"
+}

training_args.bin

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