add model v2, finetuned with triplets

README.md

@@ -1 +1,56 @@
-yotta-embeddings
+---
+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 384 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})
+
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, '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

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "yotta-test-128-bigdata2/checkpoint-14000",
+  "_name_or_path": "yotta-test-128-bigdata2-loss2.0-finetune1.0/checkpoint-100",
   "architectures": [
     "BertModel"
   ],

global_step14000/mp_rank_00_model_states.pt

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global_step14000/zero_pp_rank_0_mp_rank_00_optim_states.pt

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global_step14000/zero_pp_rank_1_mp_rank_00_optim_states.pt

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latest

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+global_step100

model.safetensors

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rng_state_0.pth

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rng_state_1.pth

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rng_state_2.pth

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tokenizer.json

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   "truncation": {
     "direction": "Right",
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     "strategy": "LongestFirst",
     "stride": 0
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training_args.bin

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