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przvl
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PopEuroBERT-binary-210m

Text Classification
Transformers
Safetensors
German
eurobert
populism
political-speech
classification
german
Bundestag
NLP
custom_code
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  4. model.safetensors +3 -0
  5. special_tokens_map.json +30 -0
  6. tokenizer.json +3 -0
  7. tokenizer_config.json +2069 -0
  8. training_args.bin +3 -0
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1
+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ language:
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+ - de
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+ pipeline_tag: text-classification
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+ tags:
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+ - populism
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+ - political-speech
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+ - classification
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+ - german
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+ - Bundestag
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+ - NLP
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+ ---
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+
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+ # PopEuroBERT-210m
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+
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+ ## Binary Populism Classifier for German Bundestag Speeches
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+
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+ ## Table of Contents
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+
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+ 1. [Overview](#overview)
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+ 2. [Usage](#usage)
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+ 3. [Training Data](#training-data)
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+ 4. [Training Procedure](#training-procedure)
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+ 5. [Evaluation](#evaluation)
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+ 6. [Limitations](#limitations)
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+ 7. [Ethical Considerations](#ethical-considerations)
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+ 8. [License](#license)
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+ 9. [Citation](#citation)
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+
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+ ## Overview
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+
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+ This model is a fine-tuned version of [EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m) on the [PopBERT](https://huggingface.co/luerhard/PopBERT) dataset (German Bundestag speeches) for **populist rhetoric classification**. It predicts whether a given speech excerpt contains populist language.
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+
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+ **Key Features:**
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+
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+ - Trained on **German Bundestag speeches** annotated for populism.
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+ - Fine-tuned using **5-fold cross-validation**.
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+ - Optimized with **decision threshold tuning**.
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+
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+ ## Usage
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+
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+ To use the model in Python:
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+
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+ ```python
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+ from transformers import AutoTokenizer
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+ from transformers import AutoModelForSequenceClassification
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+
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+
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+ model_id = "przvl/PopEuroBERT-binary-210m"
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+ tokenizer = AutoTokenizer.from_pretrained(model_id)
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+ model = AutoModelForSequenceClassification.from_pretrained(
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+ model_id, trust_remote_code=true
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+ )
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+
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+
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+ # define text to be predicted
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+ text = (
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+ "Aber Ihnen fehlt eben der Mut, Ihnen fehlen die Visionen, um sich"
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+ "gegen die Konzerne und gegen die Lobbygruppen zur Wehr zu setzen."
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+ )
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+
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+ inputs = tokenizer(text, return_tensors="pt")
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+ outputs = model(**inputs)
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+
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+ # get classification probability
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+ logits = outputs.logits
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+ probs = torch.softmax(logits, dim=-1) # shape [1, 2]
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+ populist_prob = probs[0, 1].item() # probability of class=1 (populist)
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+
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+ # use decision threshold 0.56
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+ threshold = 0.56
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+ label = "Populist" if populist_prob > threshold else "Neutral"
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+ print(f"Predicted class: {label} (Confidence: {populist_prob:.2f})")
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+ ```
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+
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+ ```text
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+ Predicted class: Populist (Confidence: 0.90)
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+ ```
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+
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+ **Use decision threshold `0.56` for balanced [performance](#evaluation).**
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+
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+ ## Training Data
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+
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+ - **Dataset:** PopBERT (annotated German Bundestag speeches).
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+ - **Preprocessing:**
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+ - Removed duplicates.
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+ - Converted labels to binary format (`populist = 1`, `neutral = 0`).
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+ - Tokenized using **EuroBERT tokenizer** with a max length of `256` tokens.
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+
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+ ## Training Procedure
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+
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+ - **Base Model:** [EuroBERT-210M](https://huggingface.co/EuroBERT/EuroBERT-210m)
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+ - **Fine-tuning Approach:**
96
+ - Used **Hugging Face Trainer** for training.
97
+ - Applied **5-fold cross-validation**.
98
+ - **Decision threshold tuning** on aggregated predictions.
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+
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+ ### Hyperparameters
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+
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+ | Parameter | Value |
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+ | --------------------- | ------- |
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+ | Learning Rate | `3e-05` |
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+ | Weight Decay | `0.0` |
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+ | Gradient Accumulation | `2` |
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+ | Warmup Ratio | `0.1` |
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+ | Epochs | `5` |
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+ | Batch Size | `16` |
110
+ | Max Length | `512` |
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+
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+ - **Mixed Precision (fp16):** Used for efficiency on GPU.
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+
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+ ## Evaluation
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+
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+ ### Test Set Results (Threshold = 0.5)
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+
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+ | Metric | Score |
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+ | --------- | ------ |
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+ | Accuracy | 75.99% |
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+ | Precision | 73.78% |
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+ | Recall | 80.66% |
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+ | F1 Score | 77.07% |
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+ | Loss | 0.4959 |
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+
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+ ### Test Set Results (Optimized Threshold = 0.56)
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+
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+ | Metric | Score |
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+ | --------- | ------ |
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+ | Accuracy | 76.00% |
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+ | Precision | 76.00% |
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+ | Recall | 76.00% |
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+ | F1 Score | 76.00% |
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+
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+ ## Limitations
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+
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+ - **Domain Specificity:**\
138
+ This model was trained on Bundestag speeches and may not generalize to all political discourse.
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+ - **Threshold Sensitivity:**\
140
+ The decision threshold (`0.56`) was optimized for this dataset but may need adjustment for other corpora.
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+ - **Potential Bias:**\
142
+ Political speech contains biases inherent in dataset labeling.
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+
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+ ## Ethical Considerations
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+
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+ - **Not suitable for high-stakes decision-making.**\
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+ This model is meant for **research purposes** in political discourse analysis.
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+ - **Bias & Context Dependence:**\
149
+ Populism is a complex concept. Automated detection should **not replace** human interpretation.
150
+ - **Transparent Use:**\
151
+ Users should document and validate model outputs in their research.
152
+
153
+ ## License
154
+
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+ Released under the Apache **2.0 License**.
156
+
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+ ## Citation
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+
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+ If you use this model or its methodology, please cite:
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+
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+ - **The original EuroBERT paper:**
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+
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+ ```bibtex
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+ @misc{boizard2025eurobertscalingmultilingualencoders,
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+ title={EuroBERT: Scaling Multilingual Encoders for European Languages},
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+ author={Nicolas Boizard and Hippolyte Gisserot-Boukhlef and Duarte M. Alves and André Martins and Ayoub Hammal and Caio Corro and Céline Hudelot and Emmanuel Malherbe and Etienne Malaboeuf and Fanny Jourdan and Gabriel Hautreux and João Alves and Kevin El-Haddad and Manuel Faysse and Maxime Peyrard and Nuno M. Guerreiro and Patrick Fernandes and Ricardo Rei and Pierre Colombo},
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+ year={2025},
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+ eprint={2503.05500},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
171
+ url={https://arxiv.org/abs/2503.05500}
172
+ }
173
+ ```
174
+
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+ - **The PopBERT dataset source:**
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+
177
+ ```bibtex
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+ @article{Erhard_Hanke_Remer_Falenska_Heiberger_2025,
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+ title={PopBERT. Detecting Populism and Its Host Ideologies in the German Bundestag},
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+ volume={33},
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+ DOI={10.1017/pan.2024.12},
182
+ number={1},
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+ journal={Political Analysis},
184
+ author={Erhard, Lukas and Hanke, Sara and Remer, Uwe and Falenska, Agnieszka and Heiberger, Raphael Heiko},
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+ year={2025},
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+ pages={1–17}
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+ }
188
+ ```
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+ "content": "<|reserved_special_token_246|>",
2037
+ "lstrip": false,
2038
+ "normalized": false,
2039
+ "rstrip": false,
2040
+ "single_word": false,
2041
+ "special": true
2042
+ },
2043
+ "128255": {
2044
+ "content": "<|reserved_special_token_247|>",
2045
+ "lstrip": false,
2046
+ "normalized": false,
2047
+ "rstrip": false,
2048
+ "single_word": false,
2049
+ "special": true
2050
+ }
2051
+ },
2052
+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|end_of_text|>",
2056
+ "extra_special_tokens": {},
2057
+ "mask_token": "<|mask|>",
2058
+ "max_length": 512,
2059
+ "model_input_names": [
2060
+ "input_ids",
2061
+ "attention_mask"
2062
+ ],
2063
+ "model_max_length": 1000000000000000019884624838656,
2064
+ "pad_to_multiple_of": null,
2065
+ "pad_token": "<|end_of_text|>",
2066
+ "pad_token_type_id": 0,
2067
+ "padding_side": "right",
2068
+ "tokenizer_class": "PreTrainedTokenizer"
2069
+ }
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:3c48b5672bb14e8245926857c466ce5febb8cea5bac75c5905fc8f8bd812f320
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+ size 5368