docs: Update results

.gitignore

@@ -171,4 +171,5 @@ model2vec_models
 results/*
 counts/*
 results_old/*
+local/*
 lightning_logs/*

README.md

@@ -96,9 +96,10 @@ Model2vec creates a small, fast, and powerful model that outperforms other stati
 
 It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using zipf weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence.
 
-Model2vec has 2 modes:
-- **Output**: behaves much a like a real sentence transformer, i.e., it uses a subword tokenizer and encodes all wordpieces. This is really quick to create, very small (30 MB), but might be less performant on some tasks.
-- **Vocab**: behaves much like GloVe or regular word2vec vectors, albeit with much better performance. These models are a bit bigger, depending on your vocabulary size, but still very fast, and are useful in situations in which you have a bit more RAM, but still need to go fast.
+Model2vec has 3 modes:
+- **Output**: behaves much like a real sentence transformer, i.e., it uses a subword tokenizer and simply encodes all wordpieces in its vocab. This is really quick to create (30 seconds on a CPU), very small (30 MB in float32), but might be less performant on some tasks.
+- **Vocab (word level)**: creates a word-level tokenizer and only encodes words that are in the vocabulary. This is a bit slower to create and creates a larger model, but might be more performant on some tasks. Note that this model can go out-of-vocabulary, which might be beneficial if your domain is very noisy
+- **Vocab (subword)**: a combination of the two methods above. In this mode, you can pass your own vocabulary, but it also uses the subword vocabulary to create representations for words not in the passed vocabulary.
 
 ## Main Features
 
@@ -224,20 +225,24 @@ print(make_leaderboard(task_scores))
 |------------------------|-------------|-----------------------------------------------------------------------|----------------|-----------------------|--------------|
 | [M2V_base_glove](https://huggingface.co/minishlab/M2V_base_glove)           | English     | Flagship embedding model based on GloVe vocab.           | GloVe        | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)                   | 102M         |
 | [M2V_base_output](https://huggingface.co/minishlab/M2V_base_output)          | English     | Flagship embedding model based on bge-base-en-v1.5 vocab. Uses a subword tokenizer.                    | Output          | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)                         | 7.5M         |
+| [M2V_base_glove_subword](https://huggingface.co/minishlab/M2V_base_glove_subword)          | English     | Flagship embedding model based on bge-base-en-v1.5 + glove vocab. Uses a subword tokenizer.                    | Output  + GloVe       | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)                         | 103M         |
 | [M2V_multilingual_output](https://huggingface.co/minishlab/M2V_multilingual_output)          | Multilingual     | Flagship multilingual embedding model based on LaBSE vocab. Uses a subword tokenizer.                    | Output          | [LaBSE](https://huggingface.co/sentence-transformers/LaBSE)                         | 471M         |
 ## Results
 
 ### Main Results
 
 Model2Vec is evaluated on MTEB, as well as two additional tasks: [PEARL](https://github.com/tigerchen52/PEARL) (a phrase representation task) and WordSim (a collection of _word_ similarity tasks). The results are shown in the table below.
 
-| Model            | Avg (All)   | Avg (MTEB) | Class | Clust | PairClass | Rank  | Ret   | STS   | Sum   | PEARL | WordSim |
-|------------------|-------------|------------|-------|-------|-----------|-------|-------|-------|-------|-------|---------|
-| all-MiniLM-L6-v2 | 56.08       | 56.09      | 62.62 | 41.94 | 82.37     | 58.04 | 41.95 | 78.90 | 30.81 | 60.83 | 49.91   |
-| M2V_base_glove   | 48.58       | 47.60      | 61.35 | 30.52 | 75.34     | 48.50 | 29.26 | 70.31 | 31.50 | 50.28 | 54.29   |
-| M2V_base_output  | 46.79       | 45.34      | 61.25 | 25.58 | 74.90     | 47.63 | 26.14 | 68.58 | 29.20 | 54.02 | 49.18   |
-| GloVe_300d       | 42.84       | 42.36      | 57.31 | 27.66 | 72.48     | 43.30 | 22.78 | 61.90 | 28.81 | 45.65 | 43.05   |
-| WL256*           | 48.88       | 49.36      | 58.98 | 33.34 | 74.00     | 52.03 | 33.12 | 73.34 | 29.05 | 48.81 | 45.16   |
+
+| Model                  | Avg (All) | Avg (MTEB) | Class  | Clust  | PairClass | Rank   | Ret    | STS    | Sum    | Pearl  | WordSim |
+|------------------------|-----------|------------|--------|--------|-----------|--------|--------|--------|--------|--------|---------|
+| all-MiniLM-L6-v2        | 56.08     | 56.09      | 62.62  | 41.94  | 82.37     | 58.04  | 41.95  | 78.90  | 30.81  | 60.83  | 49.91   |
+| M2V_base_glove_subword  | 49.06     | 46.69      | 61.27  | 30.03  | 74.71     | 49.15  | 27.16  | 69.09  | 30.08  | 56.82  | 57.99   |
+| M2V_base_glove          | 48.58     | 47.6       | 61.35  | 30.52  | 75.34     | 48.5   | 29.26  | 70.31  | 31.5   | 50.28  | 54.29   |
+| M2V_base_output         | 46.79     | 45.34      | 61.25  | 25.58  | 74.9      | 47.63  | 26.14  | 68.58  | 29.2   | 54.02  | 49.18   |
+| GloVe_300d              | 42.84     | 42.36      | 57.31  | 27.66  | 72.48     | 43.3   | 22.78  | 61.9   | 28.81  | 45.65  | 43.05   |
+| WL256*                  | 48.88     | 49.36      | 58.98  | 33.34  | 74.00     | 52.03  | 33.12  | 73.34  | 29.05  | 48.81  | 45.16   |
+
 
 <details>
   <summary>  Task Abbreviations </summary>
@@ -259,21 +264,22 @@ For readability, the MTEB task names are abbreviated as follows:
 
 In addition to the MTEB evaluation, we evaluate Model2Vec on a number of classification datasets. These are used as additional evidence to avoid overfitting to the MTEB dataset and to benchmark the speed of the model. The results are shown in the table below.
 
-| model            |   Average |     sst2 |   imdb |     trec |   ag_news |
-|:-----------------|----------:|---------:|-------:|---------:|----------:|
-| bge-base-en-v1.5 |  90.00 | 91.54 | 91.88 | 85.16 |  91.45   |
-| all-MiniLM-L6-v2 |  84.10 | 83.95 | 81.36 | 81.31 |  89.77 |
-| M2V_base_output  |  82.23 | 80.92 | 84.56 | 75.27 |  88.17  |
-| M2V_base_glove   |  80.76 | 83.07 | 85.24 | 66.12 | 88.61 |
-| WL256            |  78.48  | 76.88  | 80.12 | 69.23 |  87.68 |
-| GloVe_300d       |  77.77  | 81.68 | 84.00   | 55.67 |  89.71 |
+| Model                  | Average | SST2   | IMDB  | TREC   | AG News |
+|:-----------------------|:-------:|:------:|:-----:|:------:|:-------:|
+| bge-base-en-v1.5        | 90.00   | 91.54  | 91.88 | 85.16  | 91.45   |
+| all-MiniLM-L6-v2        | 84.10   | 83.95  | 81.36 | 81.31  | 89.77   |
+| M2V_base_output         | 82.23   | 80.92  | 84.56 | 75.27  | 88.17   |
+| M2V_base_glove_subword  | 81.95   | 82.84  | 85.96 | 70.51  | 88.49   |
+| M2V_base_glove          | 80.76   | 83.07  | 85.24 | 66.12  | 88.61   |
+| WL256                   | 78.48   | 76.88  | 80.12 | 69.23  | 87.68   |
+| GloVe_300d              | 77.77   | 81.68  | 84.00 | 55.67  | 89.71   |
 
 As can be seen, Model2Vec models outperform the GloVe and WL256 models on all classification tasks, and are competitive with the all-MiniLM-L6-v2 model, while being much faster.
 
 The figure below shows the relationship between the number of sentences per second and the average classification score. The circle sizes correspond to the number of parameters in the models (larger = more parameters).
 This plot shows that the Model2Vec models are much faster than the other models, while still being competitive in terms of classification performance with the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model.
 
-| ![Description](assets/images/speed_vs_accuracy.png) |
+| ![Description](assets/images/speed_vs_accuracy_v2.png) |
 |:--:|
 |*Figure: The average accuracy over all classification datasets plotted against sentence per second. The circle size indicates model size.*|