greek-slf model after 3 epochs

.gitignore

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+checkpoints/

1_Pooling/config.json

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+{
+  "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
+}

README.md

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+---
+language:
+  - el
+pipeline_tag: sentence-similarity
+tags:
+  - sentence-transformers
+  - feature-extraction
+  - sentence-similarity
+  - transformers
+  - longformer
+metrics:
+  - accuracy_cosinus
+  - accuracy_euclidean
+  - accuracy_manhattan
+model-index:
+  - name: st-greek-media-longformer-4096
+    results:
+      [
+        {
+          "task": { "name": "STS Benchmark", "type": "sentence-similarity" },
+          "metrics":
+            [
+              { "type": "accuracy_cosinus", "value": 0.9425676261863862 },
+              { "type": "accuracy_euclidean", "value": 0.942637030867732 },
+              { "type": "accuracy_manhattan", "value": 0.9427758402304235 },
+            ],
+          "dataset":
+            {
+              "name": "all_custom_greek_media_triplets",
+              "type": "sentence-pair",
+            },
+        },
+      ]
+---
+
+# Greek Media SLF (Sentence-Longformer)
+
+This is a [sentence-transformers](https://www.SBERT.net) based on the [Greek Media Longformer](https://huggingface.co/dimitriz/greek-media-longformer-4096) model: It maps sentences & paragraphs to a 768 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('dimitriz/st-greek-media-longformer-4096')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Usage (HuggingFace Transformers)
+
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('dimitriz/st-greek-media-longformer-4096')
+model = AutoModel.from_pretrained('dimitriz/st-greek-media-longformer-4096')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling. In this case, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_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=dimitriz/st-greek-media-longformer-4096)
+
+## Training
+
+The model was trained on a custom dataset containing triplets from the **combined** Greek 'internet', 'social-media'
+and 'press' domains, described in the paper [DACL](https://...).
+
+- The dataset was created by sampling triplets of sentences from the same domain, where the first two sentences are more
+  similar than the third one.
+- Training objective was to maximize the similarity between the first two sentences and minimize the similarity between
+  the first and the third sentence.
+- The model was trained for 3 epochs with a batch size of 2 and a maximum sequence length of 4096 tokens.
+- The model was trained on a single NVIDIA RTX A6000 GPU with 48GB of memory.
+
+The model was trained with the parameters:
+
+**DataLoader**:
+
+`torch.utils.data.dataloader.DataLoader` of length 172897 with parameters:
+
+```
+{'batch_size': 1, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
+
+```
+{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
+```
+
+Parameters of the fit()-Method:
+
+```
+{
+    "epochs": 3,
+    "evaluation_steps": 1000,
+    "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "WarmupLinear",
+    "steps_per_epoch": null,
+    "warmup_steps": 17290,
+    "weight_decay": 0.01
+}
+```
+
+## Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 4096, 'do_lower_case': False}) with Transformer model: LongformerModel
+  (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})
+)
+```
+
+## Citing & Authors
+
+```@inproceedings{...,
+  title={PTTMT},
+  author={Zaikis et al.},
+  booktitle={...},
+  year={2023}
+}
+```

config.json

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+{
+  "_name_or_path": "/home/dimitriz/.cache/torch/sentence_transformers/dimitriz_greek-media-longformer-4096",
+  "architectures": [
+    "LongformerModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "attention_window": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 4098,
+  "model_type": "longformer",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "onnx_export": false,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "sep_token_id": 2,
+  "torch_dtype": "float32",
+  "transformers_version": "4.28.0",
+  "type_vocab_size": 2,
+  "vocab_size": 52000
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.28.0",
+    "pytorch": "2.0.0+cu118"
+  }
+}

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sentence_bert_config.json

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special_tokens_map.json

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tokenizer_config.json

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