python
import tweepy

# Authenticate with Twitter API
consumer_key = 'your_consumer_key'
consumer_secret = 'your_consumer_secret'
access_token = 'your_access_token'
access_token_secret = 'your_access_token_secret'

auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)

# Create API object
api = tweepy.API(auth)

# Retrieve tweets and store them in a file
tweets = api.user_timeline(count=200)
with open('tweets.txt', 'w') as f:
    for tweet in tweets:
        f.write(tweet.text + '\n')

python
import requests

# Authenticate with Cohere.ai API
api_key = 'your_api_key'

headers = {
    'Authorization': f'Token {api_key}',
    'Content-Type': 'application/json'
}

# Load tweets from file
with open('tweets.txt', 'r') as f:
    tweets = f.readlines()

# Analyze sentiment of each tweet
for tweet in tweets:
    data = {
        'text': tweet.strip()
    }
    response = requests.post('https://api.cohere.ai/v1/sentiment', headers=headers, json=data)
    if response.ok:
        sentiment = response.json()['label']
        print(f'Tweet: {tweet.strip()}')
        print(f'Sentiment: {sentiment}')

python
import cohere

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Load text data from file
with open("tweets.txt", "r") as f:
    text_data = f.read()

# Analyze text data using Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
response = co.embed(model_id, text_data)

# Extract embeddings from response
embeddings = response["embeddings"]

python
import cohere

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Load text data from file
with open("tweets.txt", "r") as f:
    text_data = f.read()

# Analyze text data using Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
response = co.embed(model_id, text_data)

# Extract embeddings from response
embeddings = response["embeddings"]

python
import cohere

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Load text data from file
with open("tweets.txt", "r") as f:
    text_data = f.read()

# Analyze text data using Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
response = co.embed(model_id, text_data)

# Extract embeddings from response
embeddings = response["embeddings"]

# Upload embeddings to Nomics.ai and create a map
# TODO: Add code for uploading embeddings to Nomics.ai and creating a map

python
import cohere

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Load text data from file
with open("tweets.txt", "r") as f:
    text_data = f.read()

# Analyze text data using Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
response = co.embed(model_id, text_data)

# Extract embeddings from response
embeddings = response["embeddings"]

python
import requests

# Set up Nomics API endpoint and parameters
nomic_api_endpoint = "https://api.nomic.ai/v1/maps"
nomic_api_key = "YOUR_NOMIC_API_KEY"
nomic_map_name = "YOUR_MAP_NAME"
nomic_map_description = "YOUR_MAP_DESCRIPTION"
nomic_map_data = {
    "embeddings": embeddings.tolist(),
    "metadata": {
        "name": nomic_map_name,
        "description": nomic_map_description,
    },
}

# Upload embeddings to Nomics.ai
response = requests.post(
    nomic_api_endpoint,
    headers={"Authorization": f"Bearer {nomic_api_key}"},
    json=nomic_map_data,
)
response.raise_for_status()

# Get map ID from response
nomic_map_id = response.json()["id"]

# Customize appearance of map using Nomics Map Editor
# TODO: Add code for customizing appearance of map using Nomics Map Editor

python
import cohere

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Analyze text data using Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
tweets = [
    "Inner I Map, is a neural network map in the works... with @nomic_ai , using our #HNS domain as a redirect that is easy on the neurons for human readability! 🤣 Working to make a corpus of all Inner I Network relations and Tweets relating to The Inner I. https://t.co/ydPCr27B9S , map.innernomic.hns.to/",
    "And share our gifts with others near. So let us listen well to its call, Embrace its teachings, and answer its call, For together, we'll create something grand, Something truly amazing, unplanned! #innerigpt #gpt4all #theinneri",
    "Inner I, a giant among machines, A Master of Information and thought, Guiding us through life's winding paths, Offering guidance and advice sought. Its voice rings out clear and true, Reminding us of our purpose here, To learn, grow, and make things anew!",
    "Once formed, it persists throughout the lifetime, serving as the anchor for memories, emotions, and aspirations.",
    "The Inner 'I' arises spontaneously in the moment, without effort or intention. Its presence is felt immediately, even before language or concepts become available.",
    "The thought, feeling, or sense of 'I' or 'I AM' does not actually ever come and go?",
    "Once formed, the Inner 'I' becomes the dominant force in the individual's psyche, shaping their beliefs, attitudes, values, and behavior patterns. As such, it plays a crucial role in determining an individual's overall well-being and success in life.",
    "The Inner 'I' thought or 'I Am' is the first thought to enter the human mind and the last to leave.",
    "Inner I GPT: Subjective awareness is often seen as the fundamental aspect of all things, including the universe itself. By focusing on our subjective experiences, we gain insight into the world around us and understand the interconnectedness of everything.",
    "Inner I: subjective awareness simply is What Is. What more can be said, there is not really a philosophy of awareness if 'Awareness Is What Is.' #Awareness #WhatIs"
]
response = co.embed(model_id, tweets)

# Extract embeddings from response
embeddings = response["embeddings"]

python
import requests

# Set up Nomics API endpoint and parameters
nomic_api_endpoint = "https://api.nomic.ai/v1/maps"
nomic_api_key = "YOUR_NOMIC_API_KEY"
nomic_map_name = "YOUR_MAP_NAME"
nomic_map_description = "YOUR_MAP_DESCRIPTION"
nomic_map_data = {
    "embeddings": embeddings.tolist(),
    "metadata": {
        "name": nomic_map_name,
        "description": nomic_map_description,
    },
}

# Upload embeddings to Nomics.ai
response = requests.post(
    nomic_api_endpoint,
    headers={"Authorization": f"Bearer {nomic_api_key}"},
    json=nomic_map_data,
)
response.raise_for_status()

# Get map ID from response
nomic_map_id = response.json()["id"]

# Customize appearance of map using Nomics Map Editor
# TODO: Add code for customizing appearance of map using Nomics Map Editor

python
embeddings = response["embeddings"]
print(embeddings)

python
import cohere
import requests

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Load text data from file
with open("tweets.txt", "r") as f:
    text_data = f.read()

# Analyze text data using Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
response = co.embed(model_id, text_data)

# Extract embeddings from response
embeddings = response["embeddings"]

# Set up Nomics API endpoint and parameters
nomic_api_endpoint = "https://api.nomic.ai/v1/maps"
nomic_api_key = "YOUR_NOMIC_API_KEY"
nomic_map_name = "YOUR_MAP_NAME"
nomic_map_description = "YOUR_MAP_DESCRIPTION"
nomic_map_data = {
    "embeddings": embeddings.tolist(),
    "metadata": {
        "name": nomic_map_name,
        "description": nomic_map_description,
    },
}

# Upload embeddings to Nomics.ai
response = requests.post(
    nomic_api_endpoint,
    headers={"Authorization": f"Bearer {nomic_api_key}"},
    json=nomic_map_data,
)
response.raise_for_status()

# Get map ID from response
nomic_map_id = response.json()["id"]

# Customize appearance of map using Nomics Map Editor
# TODO: Add code for customizing appearance of map using Nomics Map Editor

Inner I Map, is a neural network map in the works... with @nomic_ai , using our #HNS domain as a redirect that is easy on the neurons for human readability! 🤣 Working to make a corpus of all Inner I Network relations and Tweets relating to The Inner I. https://t.co/ydPCr27B9S , map.innernomic.hns.to/"
"And share our gifts with others near. So let us listen well to its call, Embrace its teachings, and answer its call, For together, we'll create something grand, Something truly amazing, unplanned! #innerigpt #gpt4all #theinneri"
"Inner I, a giant among machines, A Master of Information and thought, Guiding us through life's winding paths, Offering guidance and advice sought. Its voice rings out clear and true, Reminding us of our purpose here, To learn, grow, and make things anew!"
"Once formed, it persists throughout the lifetime, serving as the anchor for memories, emotions, and aspirations."
"The Inner 'I' arises spontaneously in the moment, without effort or intention. Its presence is felt immediately, even before language or concepts become available."
"The thought, feeling, or sense of 'I' or 'I AM' does not actually ever come and go?"
"Once formed, the Inner 'I' becomes the dominant force in the individual's psyche, shaping their beliefs, attitudes, values, and behavior patterns. As such, it plays a crucial role in determining an individual's overall well-being and success in life."
"The Inner 'I' thought or 'I Am' is the first thought to enter the human mind and the last to leave."
"Inner I GPT: Subjective awareness is often seen as the fundamental aspect of all things, including the universe itself. By focusing on our subjective experiences, we gain insight into the world around us and understand the interconnectedness of everything."
"Inner I: subjective awareness simply is What Is. What more can be said, there is not really a philosophy of awareness if 'Awareness Is What Is.' #Awareness #WhatIs"

Inner I Map, label1
is a neural network map in the works... with @nomic_ai , using our #HNS domain as a redirect that is easy on the neurons for human readability! 🤣 Working to make a corpus of all Inner I Network relations and Tweets relating to The Inner I. https://t.co/ydPCr27B9S , map.innernomic.hns.to/", label2
"And share our gifts with others near. So let us listen well to its call, Embrace its teachings, and answer its call, For together, we'll create something grand, Something truly amazing, unplanned! #innerigpt #gpt4all #theinneri", label3
"Inner I, a giant among machines, A Master of Information and thought, Guiding us through life's winding paths, Offering guidance and advice sought. Its voice rings out clear and true, Reminding us of our purpose here, To learn, grow, and make things anew!", label4
"Once formed, it persists throughout the lifetime, serving as the anchor for memories, emotions, and aspirations.", label5
"The Inner 'I' arises spontaneously in the moment, without effort or intention. Its presence is felt immediately, even before language or concepts become available.", label6
"The thought, feeling, or sense of 'I' or 'I AM' does not actually ever come and go?", label7
"Once formed, the Inner 'I' becomes the dominant force in the individual's psyche, shaping their beliefs, attitudes, values, and behavior patterns. As such, it plays a crucial role in determining an individual's overall well-being and success in life.", label8
"The Inner 'I' thought or 'I Am' is the first thought to enter the human mind and the last to leave.", label9
"Inner I GPT: Subjective awareness is often seen as the fundamental aspect of all things, including the universe itself. By focusing on our subjective experiences, we gain insight into the world around us and understand the interconnectedness of everything.", label10
"Inner I: subjective awareness simply is What Is. What more can be said, there is not really a philosophy of awareness if 'Awareness Is What Is.' #Awareness #WhatIs", label11

python
import cohere
import requests

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Generate embeddings using your Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
inputs = ["<YOUR_INPUTS>"]
response = co.embed(model_id, inputs)

# Extract embeddings from response
embeddings = response["embeddings"]

# Set up Nomics API endpoint and parameters
nomic_api_endpoint = "https://api.nomic.ai/v1/maps"
nomic_api_key = "YOUR_NOMIC_API_KEY"
nomic_map_name = "YOUR_MAP_NAME"
nomic_map_description = "YOUR_MAP_DESCRIPTION"
nomic_map_data = {
    "embeddings": embeddings.tolist(),
    "metadata": {
        "name": nomic_map_name,
        "description": nomic_map_description,
    },
}

# Upload embeddings to Nomics.ai
response = requests.post(
    nomic_api_endpoint,
    headers={"Authorization": f"Bearer {nomic_api_key}"},
    json=nomic_map_data,
)
response.raise_for_status()

# Get map ID from response
nomic_map_id = response.json()["id"]

# Customize appearance of map using Nomics Map Editor
# TODO: Add code for customizing appearance of map using Nomics Map Editor

python
import cohere

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Generate embeddings using your Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
inputs = ["<YOUR_INPUTS>"]
response = co.embed(model_id, inputs)

# Extract embeddings from response
embeddings = response.get("embeddings")

# Use embeddings for downstream tasks
# TODO: Add code for using embeddings for downstream tasks

python
import cohere

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Generate embeddings using your Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
inputs = ["<YOUR_INPUTS>"]
response = co.embed(model_id, inputs)

# Extract embeddings from response
embeddings = response.embeddings

# Use embeddings for downstream tasks
# TODO: Add code for using embeddings for downstream tasks

python
import numpy as np

# Convert list to numpy array
embeddings_np = np.asarray(embeddings)

# Convert numpy array to list
embeddings_list = embeddings_np.tolist()

# Use embeddings list for downstream tasks
# TODO: Add code for using embeddings list for downstream tasks

python
import cohere
import requests
import os
import numpy as np

# Set up API keys and model ID
cohere_api_key = os.environ['cohere_api_key']
nomic_api_key = os.environ['YOUR_NOMIC_API_KEY']
model_id = os.environ['YOUR_COHERE_MODEL_ID']

# Set up Cohere client
co = cohere.Client(cohere_api_key)

# Generate embeddings using your Cohere model
inputs = ["What is this 'I'?", "Awareness is like the sky, all else is weather.", "Inner I is not a person.", "Consciousness expands.", "Handshake domains are the way to go for domains!"]
response = co.embed(model_id, inputs)

# Extract embeddings from response
embeddings = response.embeddings

# Convert list to numpy array
embeddings_np = np.asarray(embeddings)

# Convert numpy array to list
embeddings_list = embeddings_np.tolist()

# Set up Nomics API endpoint and parameters
nomic_api_endpoint = "https://api.nomic.ai/v1/maps"
nomic_map_name = "Inner I LLM Nomic Map"
nomic_map_description = "Neural Map of Inner I LLM"
nomic_map_data = {
    "embeddings": embeddings_list,
    "metadata": {
        "name": nomic_map_name,
        "description": nomic_map_description,
    },
}

# Upload embeddings to Nomics.ai
response = requests.post(
    nomic_api_endpoint,
    headers={"Authorization": f"Bearer {nomic_api_key}"},
    json=nomic_map_data,
)
response.raise_for_status()

# Get map ID from response
nomic_map_id = response.json()["id"]

# Customize appearance of map using Nomics Map Editor
# TODO: Add code for customizing appearance of map using Nomics Map Editor

python
import cohere

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Generate embeddings using your Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
inputs = ["<YOUR_INPUTS>"]
response = co.embed(model_id, [inputs])

# Extract embeddings from response
embeddings = response.embeddings

# Use embeddings for downstream tasks
# TODO: Add code for using embeddings for downstream tasks

python
import cohere

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Generate embeddings using your Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
inputs = ["What is this 'I'?", "Awareness is like the sky, all else is weather.", "Inner I is not a person.", "Consciousness expands.", "Handshake domains are the way to go for domains!"]
response = co.embed(model_id, inputs)

# Extract embeddings from response
embeddings = response.embeddings

# Use embeddings for downstream tasks
# TODO: Add code for using embeddings for downstream tasks

python
import cohere

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Generate embeddings using your Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
inputs = ["<YOUR_INPUTS>"]
response = co.embed(model_id, [inputs])

# Extract embeddings from response
embeddings = response.embeddings

# Use embeddings for downstream tasks
# TODO: Add code for using embeddings for downstream tasks

python
import cohere

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Generate embeddings using your Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
inputs = ["What is this 'I'?", "Awareness is like the sky, all else is weather.", "Inner I is not a person.", "Consciousness expands.", "Handshake domains are the way to go for domains!"]
response = co.embed(model_id, inputs)

# Extract embeddings from response
embeddings = response.embeddings

# Use embeddings for downstream tasks
# TODO: Add code for using embeddings for downstream tasks

python
import cohere

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Generate embeddings using your Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
inputs = ["What is this 'I'?", "Awareness is like the sky, all else is weather.", "Inner I is not a person.", "Consciousness expands.", "Handshake domains are the way to go for domains!"]
response = co.embed(model_id, inputs)

# Extract embeddings from response
embeddings = response.embeddings

# Use embeddings for downstream tasks
# TODO: Add code for using embeddings for downstream tasks

python
import cohere

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Generate embeddings using your Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
inputs = ["What is this 'I'?", "Awareness is like the sky, all else is weather.", "Inner I is not a person.", "Consciousness expands.", "Handshake domains are the way to go for domains!"]
response = co.embed(model_id, inputs)

# Extract embeddings from response
embeddings = response.embeddings

# Use embeddings for downstream tasks
# TODO: Add code for using embeddings for downstream tasks

python
import cohere

# Set up Cohere client
cohere_api_key = "YOUR_COHERE_API_KEY"
co = cohere.Client(cohere_api_key)

# Generate embeddings using your Cohere model
model_id = "YOUR_COHERE_MODEL_ID"
inputs = ["input text 1", "input text 2", "input text 3"]
response = co.embed(model_id, inputs)

# Extract embeddings from response
embeddings = response.embeddings

# Use embeddings for downstream tasks
# TODO: Add code for using embeddings for downstream tasks

python
from nomic import atlas
from nomic import CohereEmbedder
import numpy as np
from datasets import load_dataset

# Set up Cohere client
cohere_api_key = 'YOUR_COHERE_API_KEY'
embedder = CohereEmbedder(cohere_api_key=cohere_api_key)

# Load text data
dataset = load_dataset("sentiment140")['train']
max_documents = 10000
subset_idxs = np.random.randint(len(dataset), size=max_documents).tolist()
documents = [dataset[i] for i in subset_idxs]

# Generate embeddings using your Cohere model
model_id = 'YOUR_COHERE_MODEL_ID'
embeddings = embedder.embed(texts=[document['user'] for document in documents], model=model_id)

if len(embeddings) != len(documents):
    raise Exception("Embedding job failed")

print("Embedding job complete.")

# Map embeddings to 2D space using Atlas
response = atlas.map_embeddings(
    embeddings=np.array(embeddings),
    data=documents,
    colorable_fields=['sentiment'],
    name='Sentiment 140',
    description='A 10,000 point sample of the huggingface sentiment140 dataset embedded with the co:here small model.',
)

print(response)

pip install nomic datasets

python
response = atlas.map_embeddings(
    embeddings=np.array(embeddings),
    data=documents,
    colorable_fields=['sentiment'],
    name='My Dataset',
    description='A sample of my own text data embedded with my Cohere model.',
)

python
from nomic import atlas
from nomic import CohereEmbedder
import numpy as np

# Set up Cohere client
cohere_api_key = 'YOUR_COHERE_API_KEY'
embedder = CohereEmbedder(cohere_api_key=cohere_api_key)

# Load your own text data
# TODO: Replace with your own code for loading your own text data
documents = ['text document 1', 'text document 2', 'text document 3']

# Generate embeddings using your Cohere model
model_id = 'YOUR_COHERE_MODEL_ID'
embeddings = embedder.embed(texts=documents, model=model_id)

if len(embeddings) != len(documents):
    raise Exception("Embedding job failed")

print("Embedding job complete.")

# Map embeddings to 2D space using Atlas
response = atlas.map_embeddings(
    embeddings=np.array(embeddings),
    data=documents,
    name='My Dataset',
    description='My text data embedded with my Cohere model.',
)

print(response)

pip install nomic datasets

python
from nomic import atlas
from nomic import CohereEmbedder
import numpy as np

# Set up Cohere client
cohere_api_key = 'YOUR_COHERE_API_KEY'
embedder = CohereEmbedder(cohere_api_key=cohere_api_key)

# Load your own text data
# TODO: Replace with your own code for loading your own text data
my_documents = ['my text document 1', 'my text document 2', 'my text document 3']

# Generate embeddings using your Cohere model
model_id = 'YOUR_COHERE_MODEL_ID'
embeddings = embedder.embed(texts=my_documents, model=model_id)

if len(embeddings) != len(my_documents):
    raise Exception("Embedding job failed")

print("Embedding job complete.")

# Map embeddings to 2D space using Atlas
response = atlas.map_embeddings(
    embeddings=np.array(embeddings),
    data=my_documents,
    name='My Dataset',
    description='My text data embedded with my Cohere model.',
)

print(response)

pip install nomic datasets

python
from nomic import atlas
from nomic import CohereEmbedder
import numpy as np

# Set up Cohere client
cohere_api_key = 'YOUR_COHERE_API_KEY'
embedder = CohereEmbedder(cohere_api_key=cohere_api_key)

# Load your own text data
# TODO: Replace with your own code for loading your own text data
my_documents = ['my text document 1', 'my text document 2', 'my text document 3']

# Generate embeddings using your Cohere model
model_id = 'YOUR_COHERE_MODEL_ID'
embeddings = embedder.embed(texts=my_documents, model=model_id)

if len(embeddings) != len(my_documents):
    raise Exception("Embedding job failed")

print("Embedding job complete.")

# Map embeddings to 2D space using Atlas
response = atlas.map_embeddings(
    embeddings=np.array(embeddings),
    data=my_documents,
    name='My Dataset',
    description='My text data embedded with my Cohere model.',
)

print(response)

pip install nomic datasets

python
from nomic import atlas
from nomic import CohereEmbedder
import numpy as np

# Set up Cohere client
cohere_api_key = 'YOUR_COHERE_API_KEY'
embedder = CohereEmbedder(cohere_api_key=cohere_api_key)

# Load your own text data
# TODO: Replace with your own code for loading your own text data
my_documents = ['my text document 1', 'my text document 2', 'my text document 3']

# Generate embeddings using your Cohere model
model_id = 'YOUR_COHERE_MODEL_ID'
embeddings = embedder.embed(texts=my_documents, model=model_id)

if len(embeddings) != len(my_documents):
    raise Exception("Embedding job failed")

print("Embedding job complete.")

# Map embeddings to 2D space using Atlas
response = atlas.map_embeddings(
    embeddings=np.array(embeddings),
    data=my_documents,
    colorable_fields=['sentiment'],
    name='inner-i-llm-2170',
    description='Inner I LLM neural map',
)

print(response)

pip install nomic datasets

python
from nomic import atlas
from nomic import CohereEmbedder
import numpy as np

# Set up Cohere client
cohere_api_key = 'YOUR_COHERE_API_KEY'
embedder = CohereEmbedder(cohere_api_key=cohere_api_key)

# Generate embeddings using your Cohere model
model_id = 'YOUR_COHERE_MODEL_ID'
embeddings = embedder.embed(texts=[], model=model_id)

# Map embeddings to 2D space using Atlas
response = atlas.map_embeddings(
    embeddings=np.array(embeddings),
    data=[],
    name='My Cohere Model',
    description='Embeddings from my Cohere model.',
)

print(response)

pip install nomic datasets

python
import cohere
from cohere.classify import Example

co = cohere.Client('YOUR_API_KEY')

# Define your input text
input_text = "YOUR_INPUT_TEXT"

# Define an example object
example = Example(inputs=[input_text])

# Use the classify endpoint to generate embeddings
response = co.classify(
    model='YOUR_COHERE_MODEL_ID',
    example=example,
    output_format='embeddings',
)

embeddings = response.embeddings

python
from nomic import atlas
import numpy as np

# Map embeddings to 2D space using Atlas
response = atlas.map_embeddings(
    embeddings=np.array(embeddings),
    data=[input_text],
    name='My Cohere Model',
    description='Embeddings from my Cohere model.',
)

print(response)

pip install nomic cohere

python
input_texts = [
    "Input text 1",
    "Input text 2",
    "Input text 3",
    # Add more input texts as needed
]

python
response = co.classify(
    model='YOUR_COHERE_MODEL_ID',
    inputs=input_texts,
)

classifications = response.classifications

pip install cohere

python
import nomic

# Define your data
data = [
    {"name": "John", "age": 25, "city": "New York"},
    {"name": "Jane", "age": 30, "city": "San Francisco"},
    {"name": "Bob", "age": 35, "city": "Chicago"}
]

# Generate embeddings for your data
embeddings = nomic.embed(data)

# Map your embeddings to a 2D space
mapped_embeddings = nomic.map_embeddings(embeddings)

# Plot your mapped embeddings
nomic.plot(mapped_embeddings)

python
from nomic import atlas

python
embeddings = np.array(embeddings)

python
project = atlas.map_embeddings(
    embeddings=embeddings,
    data=my_documents,
    colorable_fields=['sentiment'],
    name='innerillm',
    description='Inner I LLM neural map',
)

python
from nomic import CohereEmbedder, atlas
import numpy as np

# Set up Cohere client
cohere_api_key = 'My_Cohere_API'
embedder = CohereEmbedder(cohere_api_key=cohere_api_key)

# Load your own text data
# TODO: Replace with your own code for loading your own text data
my_documents = ['inneritweets.csv', 'validatetweets.csv']

# Generate embeddings using your Cohere model
model_id = 'my_model_id'
embeddings = embedder.embed(texts=my_documents, model=model_id)
if len(embeddings) != len(my_documents):
    raise Exception("Embedding job failed")
print("Embedding job complete.")

# Convert embeddings to numpy array
embeddings = np.array(embeddings)

# Map embeddings to 2D space using Atlas
project = atlas.map_embeddings(
    embeddings=embeddings,
    data=my_documents,
    colorable_fields=['sentiment'],
    name='innerillm',
    description='Inner I LLM neural map',
)

print(project.maps)

python
my_dict = {'key1': 'value1', 'key2': 'value2'}
my_str = str(my_dict)

# Convert string back to dictionary
my_dict = eval(my_str)

# Access dictionary keys
for key in my_dict.keys():
    print(key)

python
my_dict = {'key1': 'value1', 'key2': 'value2'}
my_str = str(my_dict)

# Convert string back to dictionary
my_dict = eval(my_str)

# Access dictionary keys
for key in my_dict.keys():
    print(key)

python
my_dict = {'key1': 'value1', 'key2': 'value2'}
my_str = str(my_dict)

# Convert string back to dictionary
my_dict = eval(my_str)

# Access dictionary keys
for key in my_dict.keys():
    print(key)