Initial Model Commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,9 @@
+{
+  "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
+}

README.md

@@ -1,3 +1,92 @@
 ---
-license: mit
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+
 ---
+
+# {MODEL_NAME}
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+<!--- Describe your model here -->
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('{MODEL_NAME}')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Evaluation Results
+
+<!--- Describe how your model was evaluated -->
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+
+
+## Training
+The model was trained with the parameters:
+
+**DataLoader**:
+
+`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 10327 with parameters:
+```
+{'batch_size': 16}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
+  ```
+  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
+  ```
+
+Parameters of the fit()-Method:
+```
+{
+    "epochs": 1,
+    "evaluation_steps": 2000,
+    "evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'torch.optim.adam.Adam'>",
+    "optimizer_params": {
+        "lr": 1e-05
+    },
+    "scheduler": "warmupconstant",
+    "steps_per_epoch": null,
+    "warmup_steps": 10000,
+    "weight_decay": 0.01
+}
+```
+
+
+## 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})
+  (2): Normalize()
+)
+```
+
+## Citing & Authors
+
+<!--- Describe where people can find more information -->

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "intfloat/multilingual-e5-large",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.3.1",
+    "transformers": "4.37.2",
+    "pytorch": "2.2.0+cu121"
+  }
+}

model.safetensors

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+size 2239607176

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

mteb_metadata.md

@@ -0,0 +1,91 @@
+---
+tags:
+- mteb
+model-index:
+- name: E:\HuggingFaceDataDownloader\results\finetuned_models\2000\2000_finetune
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: DDSC/angry-tweets
+      name: MTEB AngryTweetsClassification
+      config: default
+      split: test
+      revision: 20b0e6081892e78179356fada741b7afa381443d
+    metrics:
+    - type: accuracy
+      value: 56.084049665711554
+    - type: f1
+      value: 55.198013156852625
+  - task:
+      type: BitextMining
+    dataset:
+      type: strombergnlp/bornholmsk_parallel
+      name: MTEB BornholmBitextMining
+      config: default
+      split: test
+      revision: 3bc5cfb4ec514264fe2db5615fac9016f7251552
+    metrics:
+    - type: accuracy
+      value: 47.0
+    - type: f1
+      value: 37.97365079365079
+    - type: precision
+      value: 34.48333333333334
+    - type: recall
+      value: 47.0
+  - task:
+      type: Classification
+    dataset:
+      type: danish_political_comments
+      name: MTEB DanishPoliticalCommentsClassification
+      config: default
+      split: train
+      revision: edbb03726c04a0efab14fc8c3b8b79e4d420e5a1
+    metrics:
+    - type: accuracy
+      value: 40.88398556758257
+    - type: f1
+      value: 37.604524785367076
+  - task:
+      type: Classification
+    dataset:
+      type: DDSC/lcc
+      name: MTEB LccSentimentClassification
+      config: default
+      split: test
+      revision: de7ba3406ee55ea2cc52a0a41408fa6aede6d3c6
+    metrics:
+    - type: accuracy
+      value: 59.599999999999994
+    - type: f1
+      value: 59.0619246469949
+  - task:
+      type: Classification
+    dataset:
+      type: strombergnlp/nordic_langid
+      name: MTEB NordicLangClassification
+      config: default
+      split: test
+      revision: e254179d18ab0165fdb6dbef91178266222bee2a
+    metrics:
+    - type: accuracy
+      value: 61.00333333333333
+    - type: f1
+      value: 60.45633325804296
+  - task:
+      type: Classification
+    dataset:
+      type: ScandEval/scala-da
+      name: MTEB ScalaDaClassification
+      config: default
+      split: test
+      revision: 1de08520a7b361e92ffa2a2201ebd41942c54675
+    metrics:
+    - type: accuracy
+      value: 50.43457031250001
+    - type: ap
+      value: 50.22017546538257
+    - type: f1
+      value: 50.03426509926491
+---

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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+size 5069051

special_tokens_map.json

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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "single_word": false
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+    "lstrip": false,
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+}

tokenizer.json

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tokenizer_config.json

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+      "normalized": false,
+      "rstrip": false,
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}