This is a merge model using Tie merge method.
Created using openchat 3.5 and una-cybertron-7b-v2-bf16.
Instruction template:
1import transformers
2tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5")
3
4# Single-turn
5tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids
6assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
7
8# Multi-turn
9tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids
10assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
11
12# Coding Mode
13tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids
14assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747]
This model is exceptionally well at labeling data, bringing down labeling cost to server cost. Hurray! Here is an example
User:
This is raw text, i want you to label it. in a json format.
text:
"
To build high-performing machine learning models, you need high quality data. Getting ahold of this data can be costly, complex, and inefficient. Labels created by people are needed for the majority of models to help them generate the correct predictions. To help streamline and automate this process, you can apply a machine learning model to label the data directly.
Firstly, a machine learning model is trained on a subset of raw training data that has already been labeled by humans. A model with a track record of producing precise outcomes from the information that it has learned thus far, can add labels to unlabeled data automatically. A less accurate model requires human annotators to add labels. Labels created by humans enable the model to learn and enhance its capacity to categorize new data.
Eventually, the model is able to label an increasing amount of data automatically and speed up the creation of training datasets. Of course, implementing quality control in such models is also a necessity, as with time it might drift and start producing less accurate results. In this case, human annotators can step in again.
Internal labeling (in-house), synthetic labeling (generating new data from previous datasets), programmatic labeling (using scripts), outsourcing (or freelancing) constitute a variety of data labeling methods. However, our favorite is obviously crowdsourcing — a great way to outsource data labeling and get around the drawn-out and expensive management processes. Check out our data labeling platform to learn more!"
Narwhal-7b-v3:
Here is the given text with added JSON formatted labels. Each sentence has been assigned a label indicating whether it expresses a positive, negative, or neutral sentiment:
{
"text": [
{
"content": "To build high-performing machine learning models, you need high quality data.",
"label": "neutral"
},
{
"content": "Getting ahold of this data can be costly, complex, and inefficient.",
"label": "negative"
},
{
"content": "Labels created by people are needed for the majority of models to help them generate the correct predictions.",
"label": "neutral"
},
{
"content": "To help streamline and automate this process, you can apply a machine learning model to label the data directly.",
"label": "positive"
},
{
"content": "Firstly, a machine learning model is trained on a subset of raw training data that has already been labeled by humans.",
"label": "neutral"
},
{
"content": "A model with a track record of producing precise outcomes from the information that it has learned thus far, can add labels to unlabeled data automatically.",
"label": "positive"
},
{
"content": "A less accurate model requires human annotators to add labels.",
"label": "neutral"
},
{
"content": "Labels created by humans enable the model to learn and enhance its capacity to categorize new data.",
"label": "positive"
},
{
"content": "Eventually, the model is able to label an increasing amount of data automatically and speed up the creation of training datasets.",
"label": "positive"
},
{
"content": "Of course, implementing quality control in such models is also a necessity, as with time it might drift and start producing less accurate results.",
"label": "neutral"
},
{
"content": "In this case, human annotators can step in again.",
"label": "neutral"
},
{
"content": "Internal labeling (in-house), synthetic labeling (generating new data from previous datasets), programmatic labeling (using scripts), outsourcing (or freelancing) constitute a variety of data labeling methods.",
"label": "neutral"
},
{
"content": "However, our favorite is obviously crowdsourcing — a great way to outsource data labeling and get around the drawn-out and expensive management processes.",
"label": "positive"
},
{
"content": "Check out our data labeling platform to learn more!",
"label": "neutral"
}
]
}
This JSON object now represents the original text with labeled sentiments for each sentence. You can further utilize this structured data to train sentiment analysis models or perform various analyses.
