README.md

---
license: cc-by-sa-4.0
inference: false
---

# SLIM-SA-NER

<!-- Provide a quick summary of what the model is/does. -->

**slim-sa-ner** combines two of the most popular traditional classifier functions (**Sentiment Analysis** and **Named Entity Recognition**), and reimagines them as function calls on a specialized decoder-based LLM, generating output consisting of a python dictionary with keys corresponding to sentiment, and NER identifiers, such as people, organization, and place, e.g.:  

&nbsp;&nbsp;&nbsp;&nbsp;`{'sentiment': ['positive'], people': ['..'], 'organization': ['..'],`  
&nbsp;&nbsp;&nbsp;&nbsp; `'place': ['..]}`

This 3B parameter 'combo' model is designed to illustrate the potential power of using function calls on small, specialized models to enable a single model architecture to combine the capabilities of what were traditionally two separate model architectures on an encoder.  

The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs, providing an intuitive, flexible natural language response, without complex prompting, and with improved generalization and ability to fine-tune to a specific domain use case.  


This model is fine-tuned on top of [**llmware/bling-stable-lm-3b-4e1t-v0**](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0), which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt.

For fast inference, we would recommend the 'quantized tool' version of this model, e.g.,  [**'slim-sa-ner-tool'**](https://huggingface.co/llmware/slim-sa-ner-tool).  


## Prompt format:

`function = "classify"`  
`params = "sentiment, person, organization, place"`  
`prompt = "<human> " + {text} + "\n" + `  
&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp;`"<{function}> " + {params} + "</{function}>" + "\n<bot>:"`  


<details>
<summary>Transformers Script </summary>

    model = AutoModelForCausalLM.from_pretrained("llmware/slim-sa-ner")
    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sa-ner")

    function = "classify"
    params = "topic"

    text = "Tesla stock declined yesterday 8% in premarket trading after a poorly-received event in San Francisco yesterday, in which the company indicated a likely shortfall in revenue."  
    
    prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:"

    inputs = tokenizer(prompt, return_tensors="pt")
    start_of_input = len(inputs.input_ids[0])

    outputs = model.generate(
        inputs.input_ids.to('cpu'),
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True,
        temperature=0.3,
        max_new_tokens=100
    )

    output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True)

    print("output only: ", output_only)  

    # here's the fun part
    try:
        output_only = ast.literal_eval(llm_string_output)
        print("success - converted to python dictionary automatically")
    except:
        print("fail - could not convert to python dictionary automatically - ", llm_string_output)
   
   </details>  
 
<details>  



    
<summary>Using as Function Call in LLMWare</summary>

    from llmware.models import ModelCatalog
    slim_model = ModelCatalog().load_model("llmware/slim-sa-ner")
    response = slim_model.function_call(text,params=["sentiment", "people", "organization", "place"], function="classify")

    print("llmware - llm_response: ", response)

</details>  

    
## Model Card Contact

Darren Oberst & llmware team  

[Join us on Discord](https://discord.gg/MhZn5Nc39h)