Add generated texts
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- INTRO.md +2 -1
- app.py +54 -13
- data/eval_summ_results/0-t5-base-dutch/yhavinga_cnn_dailymail_dutch/config.json +61 -0
- data/eval_summ_results/0-t5-base-dutch/yhavinga_cnn_dailymail_dutch/eval_labels_00003136_0/generated.txt +0 -0
- data/eval_summ_results/0-t5-base-dutch/yhavinga_cnn_dailymail_dutch/eval_labels_00006249_0/generated.txt +0 -0
- data/eval_summ_results/0-t5-base-dutch/yhavinga_cnn_dailymail_dutch/eval_predictions_00003136_0/generated.txt +0 -0
- data/eval_summ_results/0-t5-base-dutch/yhavinga_cnn_dailymail_dutch/eval_predictions_00006249_0/generated.txt +0 -0
- data/eval_summ_results/0-t5-base-dutch/yhavinga_cnn_dailymail_dutch/events.out.tfevents.1675612503.yeb-z390-k80.19856.0.v2 +3 -0
- data/eval_summ_results/0-t5-base-dutch/yhavinga_cnn_dailymail_dutch/generated.txt +0 -0
- data/eval_summ_results/0-t5-base-dutch/yhavinga_cnn_dailymail_dutch/special_tokens_map.json +107 -0
- data/eval_summ_results/0-t5-base-dutch/yhavinga_cnn_dailymail_dutch/test_results.json +7 -0
- data/eval_summ_results/0-t5-base-dutch/yhavinga_cnn_dailymail_dutch/tokenizer.json +0 -0
- data/eval_summ_results/0-t5-base-dutch/yhavinga_cnn_dailymail_dutch/tokenizer_config.json +112 -0
- data/eval_summ_results/1-t5-v1.1-base-dutch-uncased/yhavinga_cnn_dailymail_dutch/config.json +30 -0
- data/eval_summ_results/1-t5-v1.1-base-dutch-uncased/yhavinga_cnn_dailymail_dutch/eval_labels_00003136_0/generated.txt +0 -0
- data/eval_summ_results/1-t5-v1.1-base-dutch-uncased/yhavinga_cnn_dailymail_dutch/eval_labels_00006249_0/generated.txt +0 -0
- data/eval_summ_results/1-t5-v1.1-base-dutch-uncased/yhavinga_cnn_dailymail_dutch/eval_predictions_00003136_0/generated.txt +0 -0
- data/eval_summ_results/1-t5-v1.1-base-dutch-uncased/yhavinga_cnn_dailymail_dutch/eval_predictions_00006249_0/generated.txt +0 -0
- data/eval_summ_results/1-t5-v1.1-base-dutch-uncased/yhavinga_cnn_dailymail_dutch/events.out.tfevents.1675589234.yeb-z390-k80.16384.0.v2 +3 -0
- data/eval_summ_results/1-t5-v1.1-base-dutch-uncased/yhavinga_cnn_dailymail_dutch/generated.txt +0 -0
- data/eval_summ_results/1-t5-v1.1-base-dutch-uncased/yhavinga_cnn_dailymail_dutch/special_tokens_map.json +107 -0
- data/eval_summ_results/1-t5-v1.1-base-dutch-uncased/yhavinga_cnn_dailymail_dutch/test_results.json +7 -0
- data/eval_summ_results/1-t5-v1.1-base-dutch-uncased/yhavinga_cnn_dailymail_dutch/tokenizer.json +0 -0
- data/eval_summ_results/1-t5-v1.1-base-dutch-uncased/yhavinga_cnn_dailymail_dutch/tokenizer_config.json +112 -0
- data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/config.json +30 -0
- data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/eval_labels_00003136_0/generated.txt +0 -0
- data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/eval_labels_00006249_0/generated.txt +0 -0
- data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/eval_predictions_00003136_0/generated.txt +0 -0
- data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/eval_predictions_00006249_0/generated.txt +0 -0
- data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/events.out.tfevents.1675603189.yeb-z390-k80.18766.0.v2 +3 -0
- data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/generated.txt +0 -0
- data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/special_tokens_map.json +107 -0
- data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/test_results.json +7 -0
- data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/tokenizer.json +0 -0
- data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/tokenizer_config.json +112 -0
- data/eval_summ_results/11-t5-eff-xl-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/events.out.tfevents.1675603057.yeb-z390-k80.18371.0.v2 +3 -0
- data/eval_summ_results/11-t5-eff-xl-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/events.out.tfevents.1675652025.yeb-z390-k80.25495.0.v2 +3 -0
- data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/config.json +30 -0
- data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/eval_labels_00003136_0/generated.txt +0 -0
- data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/eval_labels_00006249_0/generated.txt +0 -0
- data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/eval_predictions_00003136_0/generated.txt +0 -0
- data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/eval_predictions_00006249_0/generated.txt +0 -0
- data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/events.out.tfevents.1675607944.yeb-z390-k80.19341.0.v2 +3 -0
- data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/generated.txt +0 -0
- data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/special_tokens_map.json +107 -0
- data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/test_results.json +7 -0
- data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/tokenizer.json +0 -0
- data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/tokenizer_config.json +112 -0
- data/eval_summ_results/13-mt5-base/yhavinga_cnn_dailymail_dutch/config.json +31 -0
- data/eval_summ_results/13-mt5-base/yhavinga_cnn_dailymail_dutch/eval_labels_00003136_0/generated.txt +0 -0
INTRO.md
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# Dutch T5 models : UL2, T5, ByT5 and Long-T5 🇳🇱🇧🇪
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TL;DR:
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During the [HuggingFace Flax/Jax community week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) in the summer of 2021,
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I was granted access to Google's TPU Research Cloud (TRC),
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# Dutch T5 models : UL2, T5, ByT5 and Long-T5 🇳🇱🇧🇪
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TL;DR: Dutch T5 and UL2 Models Trained with Google's TPU Research Cloud and mC4 Dataset Show Outstanding Performance in NLP Tasks.
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See below for model lists and comparison.
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During the [HuggingFace Flax/Jax community week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) in the summer of 2021,
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I was granted access to Google's TPU Research Cloud (TRC),
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app.py
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from
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import time
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import sqlite3
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import psutil
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import streamlit as st
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PRE_TRAINED_DB = "data/pretrained.sqlite"
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@st.
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def load_eval_data():
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conn = sqlite3.connect(PRE_TRAINED_DB)
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conn.row_factory = lambda c, r: {
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columns={"summ_rouge1": "summ Rouge1", "trans_en_nl_score": "en->nl Bleu"},
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inplace=True,
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)
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def main():
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initial_sidebar_state="collapsed", # Can be "auto", "expanded", "collapsed"
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page_icon="📑", # String, anything supported by st.image, or None.
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)
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plot_df = load_eval_data()
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with open("style.css") as f:
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st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
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| source length | 1024 | 128 |
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| target length | 142 | 128 |
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| #eval samples | 1000 | 1000 |
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### Evaluation results
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The figure below shows the evaluation scores for most models, with summarization Rouge1 on the x-axis (higher is better),
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and translation English to Dutch Bleu score on the y-axis (higher is better).
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The point size is proportional to the model size.
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"""
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)
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col1, col2 = st.columns(2)
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large_enabled = st.checkbox("large model sizes")
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_24_enabled = st.checkbox("small nl24 deep narrow sizes")
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_36_enabled = st.checkbox("base nl36 deep narrow sizes")
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_8l_enabled = st.checkbox("large nl8
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_4xl_enabled = st.checkbox("xlarge nl4
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plot_df = plot_df[
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(plot_df["name"].str.contains("ul2") & ul2_enabled)
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tokens, the sliding attention window with radius length 127 of the `long-t5` models should be able to handle this.
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""")
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with open("REMARKS.md", "r") as f:
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st.markdown(f.read())
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)
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st.markdown(
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"""###
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The models `t5-v1_1-base-dutch-english-cased` and `t5-v1_1-base-dutch-english-cased-1024` have the same model dimensions,
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but are pre-trained on different sequence lenghts, 512 and 1024 respectively.
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The evaluation loss and accuracy of the models do not look too different. Since training of the 1024 sequence length model was
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very slow and didn't converge
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loss and accuracy.
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"""
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)
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from glob import glob
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import sqlite3
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import psutil
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import streamlit as st
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PRE_TRAINED_DB = "data/pretrained.sqlite"
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@st.cache_data
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def load_eval_data():
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conn = sqlite3.connect(PRE_TRAINED_DB)
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conn.row_factory = lambda c, r: {
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columns={"summ_rouge1": "summ Rouge1", "trans_en_nl_score": "en->nl Bleu"},
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inplace=True,
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)
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# for each model, read the summary text
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for i, row in df.iterrows():
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dirs = glob(f"data/eval_summ_results/{row['id']}-{row['name']}/yhavinga_cnn_dailymail_dutch/eval_predictions*")
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try:
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file = dirs[-1] + "/generated.txt"
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with open(file, "r") as f:
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text = str(row["id"]) + " " + f.read().replace("<n>", " ")
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except Exception:
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text = "fine-tune failed, no data"
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df.at[i, "summary"] = text
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# order df by the name column desc
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df.sort_values(by="name", inplace=True, ascending=False)
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return plot_df, df
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def main():
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initial_sidebar_state="collapsed", # Can be "auto", "expanded", "collapsed"
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page_icon="📑", # String, anything supported by st.image, or None.
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)
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plot_df, df = load_eval_data()
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with open("style.css") as f:
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st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
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| source length | 1024 | 128 |
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| target length | 142 | 128 |
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| #eval samples | 1000 | 1000 |
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| WandB link | [eval_summ](https://wandb.ai/yepster/eval_dutch_cnndaily_202302_flax)|[eval_transl](https://wandb.ai/yepster/eval_dutch_ccmatrix_202302_flax) |
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On the WandB links above you can also find generated texts for each model to compare.
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### Evaluation results
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The figure below shows the evaluation scores for most models, with summarization Rouge1 on the x-axis (higher is better),
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and translation English to Dutch Bleu score on the y-axis (higher is better).
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The point size is proportional to the model size.
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UL2 models are blue,
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t5_1_1 models orange,
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Flan models red,
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mT5 green and the other models black.
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"""
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)
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col1, col2 = st.columns(2)
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large_enabled = st.checkbox("large model sizes")
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_24_enabled = st.checkbox("small nl24 deep narrow sizes")
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_36_enabled = st.checkbox("base nl36 deep narrow sizes")
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_8l_enabled = st.checkbox("large nl8 shallow sizes")
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_4xl_enabled = st.checkbox("xlarge nl4 shallow wide sizes")
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plot_df = plot_df[
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(plot_df["name"].str.contains("ul2") & ul2_enabled)
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tokens, the sliding attention window with radius length 127 of the `long-t5` models should be able to handle this.
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""")
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st.markdown("### Compare generated summaries")
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col1, col2 = st.columns(2)
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with col1:
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model_left = st.selectbox("Choose left model", df["name"], index=6)
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with col2:
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model_right = st.selectbox("Choose right model", df["name"], index=33)
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@st.cache_resource
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def get_row(model):
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return df[df["name"] == model]
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row_left = get_row(model_left)
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row_right = get_row(model_right)
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contents1 = row_left["summary"].values[0].split("\n")
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contents2 = row_right["summary"].values[0].split("\n")
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contents = list(zip(contents1, contents2))[:5]
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st.table(
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pd.DataFrame(
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contents,
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columns=[model_left, model_right],
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)
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)
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with open("REMARKS.md", "r") as f:
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st.markdown(f.read())
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)
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st.markdown(
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"""### Pre-training with sequence length 512 or 1024
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The models `t5-v1_1-base-dutch-english-cased` and `t5-v1_1-base-dutch-english-cased-1024` have the same model dimensions,
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but are pre-trained on different sequence lenghts, 512 and 1024 respectively.
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The evaluation loss and accuracy of the models do not look too different. Since training of the 1024 sequence length model was
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very slow and didn't converge, I stopped it early. The figure below shows the evaluation
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loss and accuracy.
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"""
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)
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data/eval_summ_results/0-t5-base-dutch/yhavinga_cnn_dailymail_dutch/config.json
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{
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"_name_or_path": "yhavinga/t5-base-dutch",
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"d_ff": 3072,
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"d_kv": 64,
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"d_model": 768,
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"decoder_start_token_id": 0,
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"dense_act_fn": "relu",
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"dropout_rate": 0.0,
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"eos_token_id": 1,
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"feed_forward_proj": "relu",
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"gradient_checkpointing": false,
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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"is_gated_act": false,
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"n_positions": 512,
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"num_decoder_layers": 12,
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"num_heads": 12,
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"num_layers": 12,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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"relative_attention_num_buckets": 32,
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"task_specific_params": {
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"summarization": {
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"early_stopping": true,
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+
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data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/eval_labels_00003136_0/generated.txt
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data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/eval_labels_00006249_0/generated.txt
ADDED
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data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/eval_predictions_00003136_0/generated.txt
ADDED
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data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/eval_predictions_00006249_0/generated.txt
ADDED
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|
|
data/eval_summ_results/10-t5-eff-large-8l-nedd/yhavinga_cnn_dailymail_dutch/events.out.tfevents.1675603189.yeb-z390-k80.18766.0.v2
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data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/eval_labels_00003136_0/generated.txt
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size 9290
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data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/generated.txt
ADDED
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data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/special_tokens_map.json
ADDED
@@ -0,0 +1,107 @@
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data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/test_results.json
ADDED
@@ -0,0 +1,7 @@
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{
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data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/tokenizer.json
ADDED
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data/eval_summ_results/12-t5-eff-large-8l-dutch-english-cased/yhavinga_cnn_dailymail_dutch/tokenizer_config.json
ADDED
@@ -0,0 +1,112 @@
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|
112 |
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data/eval_summ_results/13-mt5-base/yhavinga_cnn_dailymail_dutch/config.json
ADDED
@@ -0,0 +1,31 @@
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|
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{
|
2 |
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"_name_or_path": "google/mt5-base",
|
3 |
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|
4 |
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|
5 |
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|
6 |
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|
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|
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31 |
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}
|
data/eval_summ_results/13-mt5-base/yhavinga_cnn_dailymail_dutch/eval_labels_00003136_0/generated.txt
ADDED
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