Update README.md

README.md

@@ -2,7 +2,7 @@
 license: apache-2.0
 language:
 - en
-pipeline_tag: sentence-similarity
+pipeline_tag: text-classification
 inference: false
 ---
 
@@ -11,6 +11,8 @@ inference: false
 The 80M checkpoint for M2-BERT-base from the paper [Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture](https://arxiv.org/abs/2310.12109).
 This model has been pretrained with sequence length 2048, and it has been fine-tuned for long-context retrieval.
 
+Check out our [blog post]() for more on how we trained this model for long sequence.
+
 This model was trained by Jon Saad-Falcon, Dan Fu, and Simran Arora.
 
 Check out our [GitHub](https://github.com/HazyResearch/m2/tree/main) for instructions on how to download and fine-tune it!
@@ -19,21 +21,72 @@ Check out our [GitHub](https://github.com/HazyResearch/m2/tree/main) for instruc
 
 You can load this model using Hugging Face `AutoModel`:
 ```python
-from transformers import AutoModelForMaskedLM
-model = AutoModelForMaskedLM.from_pretrained("togethercomputer/m2-bert-80M-2k-retrieval", trust_remote_code=True)
+from transformers import AutoModelForSequenceClassification
+model = AutoModelForSequenceClassification.from_pretrained(
+  "togethercomputer/m2-bert-80M-2k-retrieval",
+  trust_remote_code=True
+)
 ```
 
+You should expect to see a large error message about unused parameters for FlashFFTConv.
+If you'd like to load the model with FlashFFTConv, you can check out our [GitHub](https://github.com/HazyResearch/m2/tree/main).
+
 This model generates embeddings for retrieval. The embeddings have a dimensionality of 768:
-```
-from transformers import AutoTokenizer, AutoModelForMaskedLM
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 max_seq_length = 2048
 testing_string = "Every morning, I make a cup of coffee to start my day."
-model = AutoModelForMaskedLM.from_pretrained("togethercomputer/m2-bert-80M-2k-retrieval", trust_remote_code=True)
+model = AutoModelForSequenceClassification.from_pretrained(
+  "togethercomputer/m2-bert-80M-2k-retrieval",
+  trust_remote_code=True
+)
 
-tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", model_max_length=max_seq_length)
-input_ids = tokenizer([testing_string], return_tensors="pt", padding="max_length", return_token_type_ids=False, truncation=True, max_length=max_seq_length)
+tokenizer = AutoTokenizer.from_pretrained(
+  "bert-base-uncased",
+  model_max_length=max_seq_length
+)
+input_ids = tokenizer(
+  [testing_string],
+  return_tensors="pt",
+  padding="max_length",
+  return_token_type_ids=False,
+  truncation=True,
+  max_length=max_seq_length
+)
 
 outputs = model(**input_ids)
 embeddings = outputs['sentence_embedding']
 ```
+
+You can also get embeddings from this model using the Together API as follows (you can find your API key [here](https://api.together.xyz/settings/api-keys)):
+```python
+import os
+import requests
+
+def generate_together_embeddings(text: str, model_api_string: str, api_key: str):
+    url = "https://api.together.xyz/api/v1/embeddings"
+    headers = {
+        "accept": "application/json",
+        "content-type": "application/json",
+        "Authorization": f"Bearer {api_key}"
+    }
+    session = requests.Session()
+    response = session.post(
+        url,
+        headers=headers,
+        json={
+            "input": text,
+            "model": model_api_string
+        }
+    )
+    if response.status_code != 200:
+        raise ValueError(f"Request failed with status code {response.status_code}: {response.text}")
+    return response.json()['data'][0]['embedding']
+
+print(generate_together_embeddings(
+  'Hello world',
+  'togethercomputer/m2-bert-80M-2k-retrieval',
+  os.environ['TOGETHER_API_KEY'])[:10]
+)
+```