Update README.md

README.md

@@ -6,7 +6,11 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-
+license: artistic-2.0
+datasets:
+- pszemraj/synthetic-text-similarity
+language:
+- en
 ---
 
 # BEE-spoke-data/mega-small-embed-syntheticSTS-16384
@@ -15,7 +19,16 @@ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentence
 
 <!--- Describe your model here -->
 
-## Usage (Sentence-Transformers)
+## Usage
+
+
+Regardless of method, you will need to have this specific fork of transformers installed unless you want to get errors related to padding:
+
+```sh
+pip install -U git+https://github.com/pszemraj/transformers.git@mega-upgrades --force-reinstall --no-deps
+```
+
+### Usage (Sentence-Transformers)
 
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
@@ -36,7 +49,7 @@ print(embeddings)
 
 
 
-## Usage (HuggingFace Transformers)
+### 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
@@ -72,59 +85,22 @@ 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=BEE-spoke-data/mega-small-embed-syntheticSTS-16384)
-
-
 ## Training
 The model was trained with the parameters:
 
-**DataLoader**:
-
-`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8663 with parameters:
-```
-{'batch_size': 32}
-```
 
 **Loss**:
 
 `sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters:
   ```
-  {'loss': 'MultipleNegativesRankingLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1], 'n_dims_per_step': -1}
+  {'loss': 'CosineSimilarityLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1], 'n_dims_per_step': -1}
   ```
 
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 1,
-    "evaluation_steps": 216,
-    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 867,
-    "weight_decay": 0.01
-}
-```
-
+**arch**
 
-## Full Model Architecture
 ```
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 416, 'do_lower_case': False}) with Transformer model: MegaModel 
+  (0): Transformer({'max_seq_length': 16384, 'do_lower_case': False}) with Transformer model: MegaModel 
   (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})
 )
-```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->
+```