Does anyone have experience tuning Bumblebee for better results with the roberta-based
nomic-ai/nomic-embed-text-v2-moe model? I’m seeing poor retrieval performance in Elixir, even though the same setup works well with all-MiniLM-L6-v2, and the Python version using nomic and all-MiniLM gives correct results.
# EmbeddingGenserver
@model_id "nomic-ai/nomic-embed-text-v2-moe"
...
@impl true
def handle_continue(:model_loader, _state) do
{:ok, model} =
Bumblebee.load_model({:hf, @model_id},
module: Bumblebee.Text.Roberta,
architecture: :base
)
{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, @model_id}, type: :roberta)
...
@impl true
def handle_call({:embed, input}, _from, state) do
serving =
Bumblebee.Text.text_embedding(state.model, state.tokenizer,
output_attribute: :hidden_state,
output_pool: :mean_pooling,
embedding_processor: :l2_norm
)
...
# Embedding
def ingest(input, task \\ "search_document") do
...
input_mask =
input |> Stream.map(fn i -> "#{task}: #{i}" end) |> Enum.to_list()
embedding = EmbeddingGenserver.embed(input_mask)
Stream.zip(Stream.map(input, & &1), Stream.map(embedding, & &1))
|> Stream.map(fn {text, embedding} ->
Repo.insert(%Document{text: text, embedding: embedding}, prefix: tenant)
end)
|> Stream.run()
end
...
def retrieve(text, task \\ "search_document", k \\ 3) do
...
[embedding] = EmbeddingGenserver.embed("#{task}: #{text}")
Repo.all(
from d in Document,
prefix: ^tenant,
select: {fragment("1 - (? <=> ?::vector)", d.embedding, ^embedding), d.text},
order_by: cosine_distance(d.embedding, ^embedding),
limit: ^k
)
end
....
**poor similarity**
iex> Embedding.retrieve("project")
[
{0.9799439930982597,
"Contact Timeframe: After submitting the form on ccdcare.com or via this chat, our recruiters typically reach out within 24 to 48 hours. Thank you for your patience."},
{0.9761637491887082,
"Applying for Non-Agent Positions: Thank you for your interest. Staff positions are posted on our LinkedIn account. If the position you’re seeking isn’t listed, stay updated via our social media channels."},
{0.976003851891173,
"Campaigns with High Pay: Hourly rates depend on the campaign you qualify for. Our salaries start at RD$ 260.00 p/h, with full compensation including base salary and hourly incentives. Complete the recruitment process for detailed information."}
]
tokenizer_embed = AutoTokenizer.from_pretrained('nomic-ai/nomic-embed-text-v2-moe')
model_embed = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v2-moe', trust_remote_code=True)
model_embed.eval()
def embedd(text: list):
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
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)
encoded_input = tokenizer_embed(text, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model_embed(**encoded_input)
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
return np.array(embeddings)[0]
...
ef retrieve_similar_documents_pgvector(query_text, top_k=5):
query_embedding = embedd([f"search_document: {query_text}"]).squeeze().tolist()
try:
connection = psycopg2.connect(user="postgres",
password="postgres",
host="localhost",
port="5432",
database="...")
register_vector(connection)
cursor = connection.cursor()
cursor.execute("""
SELECT text, 1 - (embedding <=> %s::vector) AS cosine_similarity
FROM ceidy.documents_copy
ORDER BY cosine_similarity DESC
LIMIT %s
""", (query_embedding, top_k))
results = cursor.fetchall()
return results # [(text, cosine_similarity), ...]
except (Exception, psycopg2.Error) as error:
print("Error during retrieval:", error)
return []
finally:
if connection:
cursor.close()
connection.close()
...
results = retrieve_similar_documents_pgvector("project", top_k=3)
for text, score in results:
print(f"Score: {score:.4f} | Text: {text}")
**the similarity looks good**
output:
Score: 0.7170 | Text: Spanish Projects: We do not currently have Spanish-language projects available. Please follow our social media for updates.
Score: 0.6636 | Text: Part-Time Projects: We currently have part-time projects available. Please share your direct number, email, ID number, and full name so a recruitment representative can assist you. Don’t miss out—apply today!
Score: 0.5672 | Text: Overseas Remote Work: At this moment, we do not have an option for overseas remote work. Please stay updated through our social media for any changes.
Really appreciate you taking the time to share the background on the 
