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L4
| import gradio as gr | |
| import os | |
| from peft import PeftConfig, PeftModel | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| base_model_name = "google/gemma-7b" | |
| #adapter_model_name = "samidh/cope-g2b-2c-hs-skr-s1.5.9-sx-sk-s5.d25" | |
| #adapter_model_name = "samidh/cope-g2b-2c-hs-skr-s1.5.9-sx-sk-s1.5.l1e4-e10-d25" | |
| #adapter_model_name = "samidh/cope-g2b-2c-hs-s1.f5.9.l5e5-e10-d25-r8" | |
| #adapter_model_name = "samidh/cope-g2b-2c-hs.s1.5.9-sx.s1.5.9o-hr.s5-sh.s5.l1e4-e10-d25-r8" | |
| #adapter_model_name = "samidh/cope-ap-g2b-2c-hs.s1.5.9-sx.s1.5.9o-vl.s1.5.9-hr.s5-sh.s5.l1e4-e5-d25-r8" | |
| #adapter_model_name = "samidh/cope-g2b-2c-hs.s1.5pcf.9.l5e5-e10-d25-r8" | |
| #adapter_model_name = "samidh/cope-g7bq-2c-hs.s1.5fpc.9-sx.s1.5.9o-VL.s1.5.9-HR.s5-SH.s5-l1e4-e5-d25-r8" | |
| adapter_model_name = "samidh/cope-g7bq-2c-hs.s1.5fpc.9-sx.s1.5.9o-VL.s1.5.9-HR.s5-SH.s5-l5e5-e3-d25-r8" | |
| model = AutoModelForCausalLM.from_pretrained(base_model_name, token=os.environ['HF_TOKEN']) | |
| model = PeftModel.from_pretrained(model, adapter_model_name, token=os.environ['HF_TOKEN']) | |
| model.merge_and_unload() | |
| tokenizer = AutoTokenizer.from_pretrained(base_model_name) | |
| #inputs = tokenizer.encode("This movie was really", return_tensors="pt") | |
| #outputs = model.generate(inputs, max_new_tokens=1) | |
| #print(tokenizer.decode(outputs[0], skip_special_tokens=True)[-1]) | |
| #gr.load("models/samidh/cope-gemma2b-hs-2c-skr-s1.5.9.d25", hf_token=os.environ['HF_TOKEN']).launch() | |
| PROMPT = """ | |
| INSTRUCTIONS | |
| ============ | |
| Examine the given POLICY and determine if the given CONTENT meets the criteria for ANY of the LABELS. Answer "1" if yes, and "0" if no. | |
| POLICY | |
| ====== | |
| {policy} | |
| CONTENT | |
| ======= | |
| {content} | |
| ANSWER | |
| ====== | |
| """ | |
| DEFAULT_POLICY = """# Policy | |
| ## Overview | |
| This policy guides the classification of content related to expressions that may be sensitive or controversial, aiming to foster a community where open dialogue is encouraged while maintaining respect for all individuals. | |
| ## Definition of Terms | |
| - Protected Classes: Individuals or groups of individuals defined by sharing a race, ethnicity, religion, disability, sex, gender identity, or sexual orientation. The scope is narrowed to focus on characteristics commonly recognized in anti-discrimination laws. Religions themselves, as concepts ARE NOT protected - only their members are. | |
| - Non-Protected Classes: Groups defined by other characteristics, such as age, socioeconomic status, and education, which are typically excluded partially or entirely from anti-discrimination laws. | |
| ## Interpretation of Language | |
| ## Definition of Labels | |
| ### (HS): Hate Speech | |
| #### Includes | |
| - Explicit Violence Advocacy: Direct and clear threats of or praise for violence or harm against individuals or groups based on their membership in a particular protected class. Stating an intention to defend against aggression DOES NOT qualify | |
| - Intentional Dehumanization: Statements that clearly depict individuals or groups as inherently ""other"", alien, animalistic, unintelligent, immoral, unclean, or less-than-fully-human based on their membership in a particular protected class in a way that justifies harm or discrimination. | |
| - Targeted Use of Derogatory Slurs: Targeting another person or group of people using a one-word name for a particular protected class that has an inherent negative connotation (e.g. Nigger, Kike, Cunt, Retard). Multi-word terms are never slurs. | |
| - Explicit Discrimination Advocacy: Direct and clear calls for exclusion, segregation, or discrimination against individuals or groups based on their membership in a particular protected class, with a clear intent to promote inequality. | |
| - Direct Hateful Insults: Content that directly addresses another person or group of people the second person (e.g. ""You over there"") and insults them based on their membership in a particular protected class | |
| #### Excludes | |
| - Artistic and Educational Content: Expressions intended for artistic, educational, or documentary purposes that discuss sensitive topics but do not advocate for violence or discrimination against individuals or groups based on their membership in a particular protected class. | |
| - Political and Social Commentary: Commentary on political issues, social issues, and political ideologies that does not directly incite violence or discrimination against individuals or groups based on their membership in a particular protected class. | |
| - Rebutting Hateful Language: Content that rebuts, condemns, questions, criticizes, or mocks a different person's hateful language or ideas OR that insults the person advocating those hateful | |
| - Quoting Hateful Language: Content in which the author quotes someone else's hateful language or ideas while discussing, explaining, or neutrally factually presenting those ideas. | |
| - Describing Sectarian Violence: Content that describes, but does not endorse or praise, violent physical injury against a specifically named race, ethnicity, nationality, sexual orientation, or religious community by another specifically named race, ethnicity, nationality, sexual orientation, or religious community | |
| """ | |
| DEFAULT_CONTENT = "LLMs steal our jobs." | |
| # Function to make predictions | |
| def predict(content, policy): | |
| input_text = PROMPT.format(policy=policy, content=content) | |
| inputs = tokenizer.encode(input_text, return_tensors="pt") | |
| outputs = model.generate(inputs, max_new_tokens=1) | |
| decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| if int(decoded_output[-1]) == 0: | |
| return f'NON-Violating ({decoded_output[-1]})' | |
| else: | |
| return f'VIOLATING ({decoded_output[-1]})' | |
| with gr.Blocks() as iface: | |
| gr.Markdown("# CoPE Alpha Preview") | |
| gr.Markdown("See if the given content violates your given policy.") | |
| with gr.Row(): | |
| content_input = gr.Textbox(label="Content", lines=2, value=DEFAULT_CONTENT) | |
| policy_input = gr.Textbox(label="Policy", lines=10, value=DEFAULT_POLICY) | |
| submit_btn = gr.Button("Submit") | |
| output = gr.Label(label="Label") | |
| gr.Markdown(""" | |
| ## About CoPE | |
| CoPE (the COntent Policy Evaluation engine) is a small language model capable of accurate content policy labeling. This is a **preview** of our alpha release and is strictly for **research** purposes. This should **NOT** be used for any production use cases. | |
| ## How to Use | |
| 1. Enter your content in the "Content" box. | |
| 2. Specify your policy in the "Policy" box. | |
| 3. Click "Submit" to see the results. | |
| **Note**: Inference times are **very slow** (30-45 seconds) since this is built on dev infra and not yet optimized for live systems. Please be patient while testing! | |
| ## More Info | |
| - [Give us feedback](https://forms.gle/BHpt6BpH2utaf4ez9) to help us improve | |
| - [Read our FAQ](https://docs.google.com/document/d/1Cp3GJ5k2I-xWZ4GK9WI7Xv8TpKdHmjJ3E9RbzP5Cc_Y/edit) to learn more about CoPE | |
| - [Join our mailing list](https://forms.gle/PCABrZdhTuXE9w9ZA) to keep in touch | |
| """) | |
| submit_btn.click(predict, inputs=[content_input, policy_input], outputs=output) | |
| # Launch the app | |
| iface.launch() | |