Add generated texts

INTRO.md

@@ -1,6 +1,7 @@
 # Dutch T5 models : UL2, T5, ByT5 and Long-T5 🇳🇱🇧🇪
 
-TL;DR: ul2-small-dutch(-english) and larger models are fit for Dutch text-to-text tasks.
+TL;DR: Dutch T5 and UL2 Models Trained with Google's TPU Research Cloud and mC4 Dataset Show Outstanding Performance in NLP Tasks.
+See below for model lists and comparison.
 
 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,
 I was granted access to Google's TPU Research Cloud (TRC),

app.py

@@ -1,6 +1,4 @@
-from functools import partial
-import time
-
+from glob import glob
 import sqlite3
 import psutil
 import streamlit as st
@@ -13,7 +11,7 @@ IMAGE_WIDTHS = 900
 PRE_TRAINED_DB = "data/pretrained.sqlite"
 
 
-@st.cache
+@st.cache_data
 def load_eval_data():
     conn = sqlite3.connect(PRE_TRAINED_DB)
     conn.row_factory = lambda c, r: {
@@ -37,7 +35,21 @@ def load_eval_data():
         columns={"summ_rouge1": "summ Rouge1", "trans_en_nl_score": "en->nl Bleu"},
         inplace=True,
     )
-    return plot_df
+    # for each model, read the summary text
+    for i, row in df.iterrows():
+        dirs = glob(f"data/eval_summ_results/{row['id']}-{row['name']}/yhavinga_cnn_dailymail_dutch/eval_predictions*")
+        try:
+            file = dirs[-1] + "/generated.txt"
+            with open(file, "r") as f:
+                text = str(row["id"]) + " " + f.read().replace("<n>", " ")
+        except Exception:
+            text = "fine-tune failed, no data"
+        df.at[i, "summary"] = text
+
+    # order df by the name column desc
+    df.sort_values(by="name", inplace=True, ascending=False)
+
+    return plot_df, df
 
 
 def main():
@@ -47,7 +59,7 @@ def main():
         initial_sidebar_state="collapsed",  # Can be "auto", "expanded", "collapsed"
         page_icon="📑",  # String, anything supported by st.image, or None.
     )
-    plot_df = load_eval_data()
+    plot_df, df = load_eval_data()
 
     with open("style.css") as f:
         st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
@@ -76,14 +88,19 @@ Fine-tuning for evaluation was done on a limited set of 50K examples from the fi
 |    source length | 1024             | 128               |
 |    target length | 142              | 128               |
 |    #eval samples | 1000             | 1000              |
-| wandb link      | [eval_summ](https://wandb.ai/yepster/eval_dutch_cnndaily_202302_flax)|[eval_transl](https://wandb.ai/yepster/eval_dutch_ccmatrix_202302_flax) |
+| WandB link      | [eval_summ](https://wandb.ai/yepster/eval_dutch_cnndaily_202302_flax)|[eval_transl](https://wandb.ai/yepster/eval_dutch_ccmatrix_202302_flax) |
+
+On the WandB links above you can also find generated texts for each model to compare. 
 
 ### Evaluation results
 
 The figure below shows the evaluation scores for most models, with summarization Rouge1 on the x-axis (higher is better),
 and translation English to Dutch Bleu score on the y-axis (higher is better).
-The point size is proportional to the model size. UL2 models are blue, Flan models
-red, mT5 green and the other models black.
+The point size is proportional to the model size.
+UL2 models are blue,
+t5_1_1 models orange,
+Flan models red,
+mT5 green and the other models black.
 """
     )
     col1, col2 = st.columns(2)
@@ -100,8 +117,8 @@ red, mT5 green and the other models black.
         large_enabled = st.checkbox("large model sizes")
         _24_enabled = st.checkbox("small nl24 deep narrow sizes")
         _36_enabled = st.checkbox("base nl36 deep narrow sizes")
-        _8l_enabled = st.checkbox("large nl8 deep wide sizes")
-        _4xl_enabled = st.checkbox("xlarge nl4 deep wide sizes")
+        _8l_enabled = st.checkbox("large nl8 shallow sizes")
+        _4xl_enabled = st.checkbox("xlarge nl4 shallow wide sizes")
 
     plot_df = plot_df[
         (plot_df["name"].str.contains("ul2") & ul2_enabled)
@@ -166,6 +183,30 @@ red, mT5 green and the other models black.
   tokens, the sliding attention window with radius length 127 of the `long-t5` models should be able to handle this.
 """)
 
+    st.markdown("### Compare generated summaries")
+    col1, col2 = st.columns(2)
+    with col1:
+        model_left = st.selectbox("Choose left model", df["name"], index=6)
+    with col2:
+        model_right = st.selectbox("Choose right model", df["name"], index=33)
+
+    @st.cache_resource
+    def get_row(model):
+        return df[df["name"] == model]
+
+    row_left = get_row(model_left)
+    row_right = get_row(model_right)
+
+    contents1 = row_left["summary"].values[0].split("\n")
+    contents2 = row_right["summary"].values[0].split("\n")
+    contents = list(zip(contents1, contents2))[:5]
+    st.table(
+        pd.DataFrame(
+            contents,
+            columns=[model_left, model_right],
+        )
+    )
+
     with open("REMARKS.md", "r") as f:
         st.markdown(f.read())
 
@@ -248,12 +289,12 @@ models to converge during fine-tuning.
     )
 
     st.markdown(
-        """### Sequence length 512 or 1024
+        """### Pre-training with sequence length 512 or 1024
 
 The models `t5-v1_1-base-dutch-english-cased` and `t5-v1_1-base-dutch-english-cased-1024` have the same model dimensions,
 but are pre-trained on different sequence lenghts, 512 and 1024 respectively.
 The evaluation loss and accuracy of the models do not look too different. Since training of the 1024 sequence length model was
-very slow and didn't converge a was was very slow, I stopped it early. The figure below shows the evaluation
+very slow and didn't converge, I stopped it early. The figure below shows the evaluation
 loss and accuracy.
 """
     )

data/eval_summ_results/0-t5-base-dutch/yhavinga_cnn_dailymail_dutch/config.json

@@ -0,0 +1,61 @@
+{
+  "_name_or_path": "yhavinga/t5-base-dutch",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.0,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "gradient_checkpointing": false,
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.23.1",
+  "use_cache": true,
+  "vocab_size": 32103
+}

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data/eval_summ_results/0-t5-base-dutch/yhavinga_cnn_dailymail_dutch/test_results.json

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data/eval_summ_results/1-t5-v1.1-base-dutch-uncased/yhavinga_cnn_dailymail_dutch/config.json

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+  "relative_attention_max_distance": 128,
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+  "tie_word_embeddings": false,
+  "transformers_version": "4.23.1",
+  "use_cache": true,
+  "vocab_size": 32103
+}

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+}

data/eval_summ_results/1-t5-v1.1-base-dutch-uncased/yhavinga_cnn_dailymail_dutch/test_results.json

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+}

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+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "eos_token": "</s>",
+  "extra_ids": 100,
+  "name_or_path": "yhavinga/t5-eff-large-8l-dutch-english-cased",
+  "pad_token": "<pad>",
+  "special_tokens_map_file": null,
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>",
+  "use_fast_tokenizer": true
+}

data/eval_summ_results/13-mt5-base/yhavinga_cnn_dailymail_dutch/config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "google/mt5-base",
+  "architectures": [
+    "MT5ForConditionalGeneration"
+  ],
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.0,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "mt5",
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "T5Tokenizer",
+  "transformers_version": "4.23.1",
+  "use_cache": true,
+  "vocab_size": 250112
+}