Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "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
+}

README.md

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+---
+base_model: jinaai/jina-embeddings-v2-base-code
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+- dot_accuracy
+- manhattan_accuracy
+- euclidean_accuracy
+- max_accuracy
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:317521
+- loss:TripletLoss
+widget:
+- source_sentence: Write a function to extract every specified element from a given
+    two dimensional list.
+  sentences:
+  - "def nCr_mod_p(n, r, p): \r\n\tif (r > n- r): \r\n\t\tr = n - r \r\n\tC = [0 for\
+    \ i in range(r + 1)] \r\n\tC[0] = 1 \r\n\tfor i in range(1, n + 1): \r\n\t\tfor\
+    \ j in range(min(i, r), 0, -1): \r\n\t\t\tC[j] = (C[j] + C[j-1]) % p \r\n\treturn\
+    \ C[r] "
+  - "import cmath\r\ndef len_complex(a,b):\r\n  cn=complex(a,b)\r\n  length=abs(cn)\r\
+    \n  return length"
+  - "def specified_element(nums, N):\r\n    result = [i[N] for i in nums]\r\n    return\
+    \ result"
+- source_sentence: Write a python function to find the kth element in an array containing
+    odd elements first and then even elements.
+  sentences:
+  - "def get_Number(n, k): \r\n    arr = [0] * n; \r\n    i = 0; \r\n    odd = 1;\
+    \ \r\n    while (odd <= n):   \r\n        arr[i] = odd; \r\n        i += 1; \r\
+    \n        odd += 2;\r\n    even = 2; \r\n    while (even <= n): \r\n        arr[i]\
+    \ = even; \r\n        i += 1;\r\n        even += 2; \r\n    return arr[k - 1]; "
+  - "def sort_matrix(M):\r\n    result = sorted(M, key=sum)\r\n    return result"
+  - "INT_BITS = 32\r\ndef left_Rotate(n,d):   \r\n    return (n << d)|(n >> (INT_BITS\
+    \ - d))  "
+- source_sentence: Write a function to remove all the words with k length in the given
+    string.
+  sentences:
+  - "def remove_tuples(test_list, K):\r\n  res = [ele for ele in test_list if len(ele)\
+    \ != K]\r\n  return (res) "
+  - "def is_Sub_Array(A,B,n,m): \r\n    i = 0; j = 0; \r\n    while (i < n and j <\
+    \ m):  \r\n        if (A[i] == B[j]): \r\n            i += 1; \r\n           \
+    \ j += 1; \r\n            if (j == m): \r\n                return True;  \r\n\
+    \        else: \r\n            i = i - j + 1; \r\n            j = 0;       \r\n\
+    \    return False; "
+  - "def remove_length(test_str, K):\r\n  temp = test_str.split()\r\n  res = [ele\
+    \ for ele in temp if len(ele) != K]\r\n  res = ' '.join(res)\r\n  return (res) "
+- source_sentence: Write a function to find the occurence of characters 'std' in the
+    given string 1. list item 1. list item 1. list item 2. list item 2. list item
+    2. list item
+  sentences:
+  - "def magic_square_test(my_matrix):\r\n    iSize = len(my_matrix[0])\r\n    sum_list\
+    \ = []\r\n    sum_list.extend([sum (lines) for lines in my_matrix])   \r\n   \
+    \ for col in range(iSize):\r\n        sum_list.append(sum(row[col] for row in\
+    \ my_matrix))\r\n    result1 = 0\r\n    for i in range(0,iSize):\r\n        result1\
+    \ +=my_matrix[i][i]\r\n    sum_list.append(result1)      \r\n    result2 = 0\r\
+    \n    for i in range(iSize-1,-1,-1):\r\n        result2 +=my_matrix[i][i]\r\n\
+    \    sum_list.append(result2)\r\n    if len(set(sum_list))>1:\r\n        return\
+    \ False\r\n    return True"
+  - "def count_occurance(s):\r\n  count=0\r\n  for i in range(len(s)):\r\n    if (s[i]==\
+    \ 's' and s[i+1]=='t' and s[i+2]== 'd'):\r\n      count = count + 1\r\n  return\
+    \ count"
+  - "def power(a,b):\r\n\tif b==0:\r\n\t\treturn 1\r\n\telif a==0:\r\n\t\treturn 0\r\
+    \n\telif b==1:\r\n\t\treturn a\r\n\telse:\r\n\t\treturn a*power(a,b-1)"
+- source_sentence: Write a function to find sum and average of first n natural numbers.
+  sentences:
+  - "def long_words(n, str):\r\n    word_len = []\r\n    txt = str.split(\" \")\r\n\
+    \    for x in txt:\r\n        if len(x) > n:\r\n            word_len.append(x)\r\
+    \n    return word_len\t"
+  - "def long_words(n, str):\r\n    word_len = []\r\n    txt = str.split(\" \")\r\n\
+    \    for x in txt:\r\n        if len(x) > n:\r\n            word_len.append(x)\r\
+    \n    return word_len\t"
+  - "def sum_average(number):\r\n total = 0\r\n for value in range(1, number + 1):\r\
+    \n    total = total + value\r\n average = total / number\r\n return (total,average)"
+model-index:
+- name: SentenceTransformer based on jinaai/jina-embeddings-v2-base-code
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: sts dev
+      type: sts-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.4794644366223058
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.3189056517809246
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.49047258618028966
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.47951587657351136
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.49047258618028966
+      name: Max Accuracy
+---
+
+# SentenceTransformer based on jinaai/jina-embeddings-v2-base-code
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jinaai/jina-embeddings-v2-base-code](https://huggingface.co/jinaai/jina-embeddings-v2-base-code). It maps sentences & paragraphs to a 768-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:** [jinaai/jina-embeddings-v2-base-code](https://huggingface.co/jinaai/jina-embeddings-v2-base-code) <!-- at revision fa8baa2e34f0fe28aae07f9bd7bcd1215de41dce -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 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: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, '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})
+)
+```
+
+## 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("Nutanix/jina-embeddings-v2-base-code-mbpp")
+# Run inference
+sentences = [
+    'Write a function to find sum and average of first n natural numbers.',
+    'def sum_average(number):\r\n total = 0\r\n for value in range(1, number + 1):\r\n    total = total + value\r\n average = total / number\r\n return (total,average)',
+    'def long_words(n, str):\r\n    word_len = []\r\n    txt = str.split(" ")\r\n    for x in txt:\r\n        if len(x) > n:\r\n            word_len.append(x)\r\n    return word_len\t',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# 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
+* Dataset: `sts-dev`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| cosine_accuracy    | 0.4795     |
+| dot_accuracy       | 0.3189     |
+| manhattan_accuracy | 0.4905     |
+| euclidean_accuracy | 0.4795     |
+| **max_accuracy**   | **0.4905** |
+
+<!--
+## 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 Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `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
+- `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`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: True
+- `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, '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_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss | sts-dev_max_accuracy |
+|:------:|:-----:|:-------------:|:--------------------:|
+| 0      | 0     | -             | 0.5027               |
+| 0.0050 | 100   | 5.0           | -                    |
+| 0.0101 | 200   | 5.0           | -                    |
+| 0.0151 | 300   | 4.9999        | -                    |
+| 0.0202 | 400   | 5.0001        | -                    |
+| 0.0252 | 500   | 5.0           | -                    |
+| 0.0302 | 600   | 5.0           | -                    |
+| 0.0353 | 700   | 4.9999        | -                    |
+| 0.0403 | 800   | 5.0001        | -                    |
+| 0.0453 | 900   | 5.0           | -                    |
+| 0.0504 | 1000  | 5.0001        | -                    |
+| 0.0554 | 1100  | 4.9999        | -                    |
+| 0.0605 | 1200  | 5.0           | -                    |
+| 0.0655 | 1300  | 5.0           | -                    |
+| 0.0705 | 1400  | 4.9999        | -                    |
+| 0.0756 | 1500  | 5.0           | -                    |
+| 0.0806 | 1600  | 4.9999        | -                    |
+| 0.0857 | 1700  | 5.0001        | -                    |
+| 0.0907 | 1800  | 5.0001        | -                    |
+| 0.0957 | 1900  | 5.0           | -                    |
+| 0.1008 | 2000  | 5.0001        | -                    |
+| 0.1058 | 2100  | 5.0           | -                    |
+| 0.1109 | 2200  | 4.9999        | -                    |
+| 0.1159 | 2300  | 4.9999        | -                    |
+| 0.1209 | 2400  | 5.0           | -                    |
+| 0.1260 | 2500  | 5.0           | -                    |
+| 0.1310 | 2600  | 5.0001        | -                    |
+| 0.1360 | 2700  | 4.9999        | -                    |
+| 0.1411 | 2800  | 5.0001        | -                    |
+| 0.1461 | 2900  | 5.0001        | -                    |
+| 0.1512 | 3000  | 5.0           | -                    |
+| 0.1562 | 3100  | 5.0001        | -                    |
+| 0.1612 | 3200  | 4.9999        | -                    |
+| 0.1663 | 3300  | 5.0001        | -                    |
+| 0.1713 | 3400  | 4.9999        | -                    |
+| 0.1764 | 3500  | 4.9999        | -                    |
+| 0.1814 | 3600  | 4.9999        | -                    |
+| 0.1864 | 3700  | 5.0           | -                    |
+| 0.1915 | 3800  | 4.9999        | -                    |
+| 0.1965 | 3900  | 5.0           | -                    |
+| 0.2016 | 4000  | 5.0           | -                    |
+| 0.2066 | 4100  | 5.0           | -                    |
+| 0.2116 | 4200  | 5.0002        | -                    |
+| 0.2167 | 4300  | 5.0002        | -                    |
+| 0.2217 | 4400  | 5.0           | -                    |
+| 0.2267 | 4500  | 5.0001        | -                    |
+| 0.2318 | 4600  | 5.0001        | -                    |
+| 0.2368 | 4700  | 5.0001        | -                    |
+| 0.2419 | 4800  | 4.9998        | -                    |
+| 0.2469 | 4900  | 5.0           | -                    |
+| 0.2519 | 5000  | 4.9999        | -                    |
+| 0.2570 | 5100  | 4.9999        | -                    |
+| 0.2620 | 5200  | 5.0001        | -                    |
+| 0.2671 | 5300  | 5.0001        | -                    |
+| 0.2721 | 5400  | 4.9999        | -                    |
+| 0.2771 | 5500  | 5.0           | -                    |
+| 0.2822 | 5600  | 5.0002        | -                    |
+| 0.2872 | 5700  | 5.0002        | -                    |
+| 0.2923 | 5800  | 4.9999        | -                    |
+| 0.2973 | 5900  | 5.0           | -                    |
+| 0.3023 | 6000  | 5.0001        | -                    |
+| 0.3074 | 6100  | 4.9999        | -                    |
+| 0.3124 | 6200  | 4.9997        | -                    |
+| 0.3174 | 6300  | 4.9999        | -                    |
+| 0.3225 | 6400  | 5.0           | -                    |
+| 0.3275 | 6500  | 4.9998        | -                    |
+| 0.3326 | 6600  | 5.0           | -                    |
+| 0.3376 | 6700  | 4.9998        | -                    |
+| 0.3426 | 6800  | 5.0001        | -                    |
+| 0.3477 | 6900  | 5.0002        | -                    |
+| 0.3527 | 7000  | 5.0           | -                    |
+| 0.3578 | 7100  | 4.9998        | -                    |
+| 0.3628 | 7200  | 5.0003        | -                    |
+| 0.3678 | 7300  | 5.0           | -                    |
+| 0.3729 | 7400  | 5.0002        | -                    |
+| 0.3779 | 7500  | 5.0           | -                    |
+| 0.3829 | 7600  | 5.0001        | -                    |
+| 0.3880 | 7700  | 5.0002        | -                    |
+| 0.3930 | 7800  | 5.0001        | -                    |
+| 0.3981 | 7900  | 5.0001        | -                    |
+| 0.4031 | 8000  | 5.0           | -                    |
+| 0.4081 | 8100  | 4.9998        | -                    |
+| 0.4132 | 8200  | 4.9999        | -                    |
+| 0.4182 | 8300  | 5.0001        | -                    |
+| 0.4233 | 8400  | 5.0001        | -                    |
+| 0.4283 | 8500  | 5.0           | -                    |
+| 0.4333 | 8600  | 5.0002        | -                    |
+| 0.4384 | 8700  | 5.0001        | -                    |
+| 0.4434 | 8800  | 5.0           | -                    |
+| 0.4485 | 8900  | 4.9996        | -                    |
+| 0.4535 | 9000  | 4.9999        | -                    |
+| 0.4585 | 9100  | 5.0           | -                    |
+| 0.4636 | 9200  | 4.9999        | -                    |
+| 0.4686 | 9300  | 4.9999        | -                    |
+| 0.4736 | 9400  | 4.9998        | -                    |
+| 0.4787 | 9500  | 5.0001        | -                    |
+| 0.4837 | 9600  | 4.9998        | -                    |
+| 0.4888 | 9700  | 4.9999        | -                    |
+| 0.4938 | 9800  | 5.0           | -                    |
+| 0.4988 | 9900  | 4.9998        | -                    |
+| 0.5039 | 10000 | 5.0           | -                    |
+| 0.5089 | 10100 | 5.0002        | -                    |
+| 0.5140 | 10200 | 5.0003        | -                    |
+| 0.5190 | 10300 | 4.9998        | -                    |
+| 0.5240 | 10400 | 4.9999        | -                    |
+| 0.5291 | 10500 | 5.0           | -                    |
+| 0.5341 | 10600 | 4.9999        | -                    |
+| 0.5392 | 10700 | 5.0           | -                    |
+| 0.5442 | 10800 | 5.0001        | -                    |
+| 0.5492 | 10900 | 4.9999        | -                    |
+| 0.5543 | 11000 | 5.0           | -                    |
+| 0.5593 | 11100 | 4.9999        | -                    |
+| 0.5643 | 11200 | 5.0           | -                    |
+| 0.5694 | 11300 | 4.9999        | -                    |
+| 0.5744 | 11400 | 4.9997        | -                    |
+| 0.5795 | 11500 | 5.0002        | -                    |
+| 0.5845 | 11600 | 4.9999        | -                    |
+| 0.5895 | 11700 | 5.0001        | -                    |
+| 0.5946 | 11800 | 5.0001        | -                    |
+| 0.5996 | 11900 | 5.0004        | -                    |
+| 0.6047 | 12000 | 4.9998        | -                    |
+| 0.6097 | 12100 | 5.0002        | -                    |
+| 0.6147 | 12200 | 4.9998        | -                    |
+| 0.6198 | 12300 | 5.0001        | -                    |
+| 0.6248 | 12400 | 5.0001        | -                    |
+| 0.6298 | 12500 | 5.0001        | -                    |
+| 0.6349 | 12600 | 4.9999        | -                    |
+| 0.6399 | 12700 | 5.0001        | -                    |
+| 0.6450 | 12800 | 4.9999        | -                    |
+| 0.6500 | 12900 | 5.0001        | -                    |
+| 0.6550 | 13000 | 4.9999        | -                    |
+| 0.6601 | 13100 | 5.0002        | -                    |
+| 0.6651 | 13200 | 5.0001        | -                    |
+| 0.6702 | 13300 | 5.0002        | -                    |
+| 0.6752 | 13400 | 4.9997        | -                    |
+| 0.6802 | 13500 | 5.0001        | -                    |
+| 0.6853 | 13600 | 4.9996        | -                    |
+| 0.6903 | 13700 | 4.9999        | -                    |
+| 0.6954 | 13800 | 5.0002        | -                    |
+| 0.7004 | 13900 | 4.9997        | -                    |
+| 0.7054 | 14000 | 5.0           | -                    |
+| 0.7105 | 14100 | 5.0001        | -                    |
+| 0.7155 | 14200 | 5.0001        | -                    |
+| 0.7205 | 14300 | 4.9999        | -                    |
+| 0.7256 | 14400 | 4.9999        | -                    |
+| 0.7306 | 14500 | 4.9998        | -                    |
+| 0.7357 | 14600 | 5.0           | -                    |
+| 0.7407 | 14700 | 5.0002        | -                    |
+| 0.7457 | 14800 | 5.0001        | -                    |
+| 0.7508 | 14900 | 4.9998        | -                    |
+| 0.7558 | 15000 | 5.0002        | -                    |
+| 0.7609 | 15100 | 5.0002        | -                    |
+| 0.7659 | 15200 | 5.0           | -                    |
+| 0.7709 | 15300 | 5.0002        | -                    |
+| 0.7760 | 15400 | 5.0           | -                    |
+| 0.7810 | 15500 | 5.0001        | -                    |
+| 0.7861 | 15600 | 5.0           | -                    |
+| 0.7911 | 15700 | 5.0004        | -                    |
+| 0.7961 | 15800 | 5.0           | -                    |
+| 0.8012 | 15900 | 5.0001        | -                    |
+| 0.8062 | 16000 | 5.0003        | -                    |
+| 0.8112 | 16100 | 4.9999        | -                    |
+| 0.8163 | 16200 | 5.0           | -                    |
+| 0.8213 | 16300 | 4.9999        | -                    |
+| 0.8264 | 16400 | 5.0           | -                    |
+| 0.8314 | 16500 | 4.9999        | -                    |
+| 0.8364 | 16600 | 4.9998        | -                    |
+| 0.8415 | 16700 | 4.9998        | -                    |
+| 0.8465 | 16800 | 5.0002        | -                    |
+| 0.8516 | 16900 | 4.9999        | -                    |
+| 0.8566 | 17000 | 4.9999        | -                    |
+| 0.8616 | 17100 | 4.9997        | -                    |
+| 0.8667 | 17200 | 5.0001        | -                    |
+| 0.8717 | 17300 | 4.9999        | -                    |
+| 0.8768 | 17400 | 5.0001        | -                    |
+| 0.8818 | 17500 | 4.9999        | -                    |
+| 0.8868 | 17600 | 5.0001        | -                    |
+| 0.8919 | 17700 | 5.0001        | -                    |
+| 0.8969 | 17800 | 5.0001        | -                    |
+| 0.9019 | 17900 | 4.9996        | -                    |
+| 0.9070 | 18000 | 5.0001        | -                    |
+| 0.9120 | 18100 | 4.9997        | -                    |
+| 0.9171 | 18200 | 5.0001        | -                    |
+| 0.9221 | 18300 | 4.9998        | -                    |
+| 0.9271 | 18400 | 4.9997        | -                    |
+| 0.9322 | 18500 | 4.9999        | -                    |
+| 0.9372 | 18600 | 5.0001        | -                    |
+| 0.9423 | 18700 | 5.0004        | -                    |
+| 0.9473 | 18800 | 4.9997        | -                    |
+| 0.9523 | 18900 | 4.9999        | -                    |
+| 0.9574 | 19000 | 5.0001        | -                    |
+| 0.9624 | 19100 | 4.9999        | -                    |
+| 0.9674 | 19200 | 5.0           | -                    |
+| 0.9725 | 19300 | 4.9999        | -                    |
+| 0.9775 | 19400 | 4.9999        | -                    |
+| 0.9826 | 19500 | 4.9999        | -                    |
+| 0.9876 | 19600 | 4.9998        | -                    |
+| 0.9926 | 19700 | 5.0           | -                    |
+| 0.9977 | 19800 | 4.9999        | -                    |
+| 1.0    | 19846 | -             | 0.4905               |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.0.1
+- Transformers: 4.40.0
+- PyTorch: 2.3.0+cu121
+- Accelerate: 0.33.0
+- Datasets: 2.20.0
+- 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",
+}
+```
+
+#### TripletLoss
+```bibtex
+@misc{hermans2017defense,
+    title={In Defense of the Triplet Loss for Person Re-Identification}, 
+    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
+    year={2017},
+    eprint={1703.07737},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+<!--
+## 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 +1,35 @@
+{
+  "_name_or_path": "jinaai/jina-embeddings-v2-base-code",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "jinaai/jina-bert-v2-qk-post-norm--configuration_bert.JinaBertConfig",
+    "AutoModel": "jinaai/jina-bert-v2-qk-post-norm--modeling_bert.JinaBertModel",
+    "AutoModelForMaskedLM": "jinaai/jina-bert-v2-qk-post-norm--modeling_bert.JinaBertForMaskedLM",
+    "AutoModelForSequenceClassification": "jinaai/jina-bert-v2-qk-post-norm--modeling_bert.JinaBertForSequenceClassification"
+  },
+  "classifier_dropout": null,
+  "emb_pooler": "mean",
+  "feed_forward_type": "geglu",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 8192,
+  "model_max_length": 8192,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "alibi",
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 61056
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.40.0",
+    "pytorch": "2.3.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:51884e1118e76a16cc5b327d93689c19af318de5a6c913e227201952bbfca9fb
+size 555344752

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "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

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "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

@@ -0,0 +1,57 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 8192,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}