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@@ -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": 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
+}

README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+
+---
+
+# {MODEL_NAME}
+
+This is a [sentence-transformers](https://www.SBERT.net) 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('{MODEL_NAME}')
+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('{MODEL_NAME}')
+model = AutoModel.from_pretrained('{MODEL_NAME}')
+
+# 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={MODEL_NAME})
+
+
+## Training
+The model was trained with the parameters:
+
+**DataLoader**:
+
+`torch.utils.data.dataloader.DataLoader` of length 1950 with parameters:
+```
+{'batch_size': 256, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
+  ```
+  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
+  ```
+
+Parameters of the fit()-Method:
+```
+{
+    "epochs": 40,
+    "evaluation_steps": 0,
+    "evaluator": "NoneType",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "warmuplinear",
+    "steps_per_epoch": null,
+    "warmup_steps": 7800,
+    "weight_decay": 0.01
+}
+```
+
+
+## Full Model Architecture
+```
+SentenceTransformerCustom(
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XmodModel 
+  (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})
+)
+```
+
+## Citing & Authors
+
+<!--- Describe where people can find more information -->

config.json

@@ -0,0 +1,116 @@
+{
+  "_name_or_path": "facebook/xmod-base",
+  "adapter_layer_norm": false,
+  "adapter_reduction_factor": 2,
+  "adapter_reuse_layer_norm": true,
+  "architectures": [
+    "XmodModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "default_language": "en_XX",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "languages": [
+    "en_XX",
+    "id_ID",
+    "vi_VN",
+    "ru_RU",
+    "fa_IR",
+    "sv_SE",
+    "ja_XX",
+    "fr_XX",
+    "de_DE",
+    "ro_RO",
+    "ko_KR",
+    "hu_HU",
+    "es_XX",
+    "fi_FI",
+    "uk_UA",
+    "da_DK",
+    "pt_XX",
+    "no_XX",
+    "th_TH",
+    "pl_PL",
+    "bg_BG",
+    "nl_XX",
+    "zh_CN",
+    "he_IL",
+    "el_GR",
+    "it_IT",
+    "sk_SK",
+    "hr_HR",
+    "tr_TR",
+    "ar_AR",
+    "cs_CZ",
+    "lt_LT",
+    "hi_IN",
+    "zh_TW",
+    "ca_ES",
+    "ms_MY",
+    "sl_SI",
+    "lv_LV",
+    "ta_IN",
+    "bn_IN",
+    "et_EE",
+    "az_AZ",
+    "sq_AL",
+    "sr_RS",
+    "kk_KZ",
+    "ka_GE",
+    "tl_XX",
+    "ur_PK",
+    "is_IS",
+    "hy_AM",
+    "ml_IN",
+    "mk_MK",
+    "be_BY",
+    "la_VA",
+    "te_IN",
+    "eu_ES",
+    "gl_ES",
+    "mn_MN",
+    "kn_IN",
+    "ne_NP",
+    "sw_KE",
+    "si_LK",
+    "mr_IN",
+    "af_ZA",
+    "gu_IN",
+    "cy_GB",
+    "eo_EO",
+    "km_KH",
+    "ky_KG",
+    "uz_UZ",
+    "ps_AF",
+    "pa_IN",
+    "ga_IE",
+    "ha_NG",
+    "am_ET",
+    "lo_LA",
+    "ku_TR",
+    "so_SO",
+    "my_MM",
+    "or_IN",
+    "sa_IN"
+  ],
+  "layer_norm_eps": 1e-05,
+  "ln_before_adapter": true,
+  "max_position_embeddings": 514,
+  "model_type": "xmod",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "pre_norm": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.0.dev0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.35.0.dev0",
+    "pytorch": "2.1.0+cu121"
+  }
+}

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"
+  }
+]

pytorch_model.bin

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sentence_bert_config.json

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+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer.json

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tokenizer_config.json

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+      "normalized": false,
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+  "additional_special_tokens": [],
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
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+  "pad_token": "<pad>",
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+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}