Update README.md

README.md

@@ -5,15 +5,47 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-
+datasets:
+- embedding-data/QQP_triplets
+language:
+- en
+license: mit
 ---
 
-# charlesdedampierre/bunka-embedding
+
+# charlesdedampierre/bunka-llms
 
 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 in Bunkatopics
+
+You can use this LLM in the BunkaTopics package as following:
+
+```
+pip install bunkatopics
+```
+
+```python
+from bunkatopics import Bunka
+import random
+from datasets import load_dataset
+
+dataset = load_dataset("rguo123/trump_tweets")['train']['content']
+full_docs = random.sample(dataset, 10000)
+
+from langchain.embeddings import HuggingFaceEmbeddings
+
+embedding_model = HuggingFaceEmbeddings(model_name="charlesdedampierre/bunka-embedding")
+
+bunka = Bunka(model_hf=embedding_model)
+
+bunka.fit(full_docs)
+df_topics = bunka.get_topics(n_clusters = 20)
+```
+
 ## Usage (Sentence-Transformers)
 
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
@@ -54,8 +86,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('charlesdedampierre/bunka-embedding')
-model = AutoModel.from_pretrained('charlesdedampierre/bunka-embedding')
+tokenizer = AutoTokenizer.from_pretrained('charlesdedampierre/bunka-llms')
+model = AutoModel.from_pretrained('charlesdedampierre/bunka-llms')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -77,43 +109,8 @@ print(sentence_embeddings)
 
 <!--- 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=charlesdedampierre/bunka-embedding)
-
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=charlesdedampierre/bunka-llms)
 
-## Training
-The model was trained with the parameters:
-
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 3181 with parameters:
-```
-{'batch_size': 16, '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": 5,
-    "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": 1590,
-    "weight_decay": 0.01
-}
-```
 
 
 ## Full Model Architecture