Upload fine-tuned chart reranker model

README.md

@@ -6,14 +6,14 @@ tags:
 - generated_from_trainer
 - dataset_size:7779
 - loss:BinaryCrossEntropyLoss
-base_model: Alibaba-NLP/gte-reranker-modernbert-base
+base_model: cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
 pipeline_tag: text-ranking
 library_name: sentence-transformers
 metrics:
 - pearson
 - spearman
 model-index:
-- name: CrossEncoder based on Alibaba-NLP/gte-reranker-modernbert-base
+- name: CrossEncoder based on cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
   results:
   - task:
       type: cross-encoder-correlation
@@ -23,22 +23,22 @@ model-index:
       type: validation
     metrics:
     - type: pearson
-      value: 0.8888985992978667
+      value: 0.7896719778282094
       name: Pearson
     - type: spearman
-      value: 0.8845425048973017
+      value: 0.8408691182123382
       name: Spearman
 ---
 
-# CrossEncoder based on Alibaba-NLP/gte-reranker-modernbert-base
+# CrossEncoder based on cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
 
-This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/mmarco-mMiniLMv2-L12-H384-v1](https://huggingface.co/cross-encoder/mmarco-mMiniLMv2-L12-H384-v1) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
 
 ## Model Details
 
 ### Model Description
 - **Model Type:** Cross Encoder
-- **Base model:** [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) <!-- at revision f7481e6055501a30fb19d090657df9ec1f79ab2c -->
+- **Base model:** [cross-encoder/mmarco-mMiniLMv2-L12-H384-v1](https://huggingface.co/cross-encoder/mmarco-mMiniLMv2-L12-H384-v1) <!-- at revision 1427fd652930e4ba29e8149678df786c240d8825 -->
 - **Maximum Sequence Length:** 512 tokens
 - **Number of Output Labels:** 1 label
 <!-- - **Training Dataset:** Unknown -->
@@ -70,11 +70,11 @@ from sentence_transformers import CrossEncoder
 model = CrossEncoder("cross_encoder_model_id")
 # Get scores for pairs of texts
 pairs = [
-    ['Cohere funding history: amounts raised by round', 'Title: "Cohere Overview"\nCollections: Companies\nChart Type: company:private\nSources: S&P Global'],
-    ['villes sympa à voir entre turin et come', 'Title: "Turin F.C. Schedule"\nCollections: Soccer\nChart Type: schedule:soccer_team_v2'],
-    ['Current housing inventory in Chattanooga, TN', 'Title: "Tusculum, TN Inventory - House"\nCollections: Residential Real Estate\nDatasets: RegionalRealEstateIndicators\nChart Type: timeseries:eav_v2\nCanonical forms: "Inventory"="inventory_seasonally_unadjusted"\nSources: Redfin'],
-    ["What's Tesla's raw material inventory?", 'Title: "Tesla Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Tesla"="Tesla, Inc.", "Overview"="Stock Overview"\nSources: S&P Global'],
-    ['current weather in hong kong', 'Title: "Hong Kong Weather"\nCollections: Weather Forecasts\nChart Type: weather:international_forecast\nSources: OpenWeather'],
+    ['Which neighborhoods in Yonkers have the highest one-bedroom apartment rents?', 'Title: "Yonkers, NY 2 Bedroom Apartment Rent"\nCollections: Residential Real Estate\nDatasets: RegionalApartmentRentIndicators\nChart Type: timeseries:eav_v2\nCanonical forms: "Rent"="average_rent_price"\nSources: ApartmentList'],
+    ['MLB team rankings', 'Title: "Multi-Chem Ltd. Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Multi-Chem Ltd."="Multi-Chem Limited", "Overview"="Stock Overview"\nSources: S&P Global'],
+    ['Eli Lilly dividend history 2014 to 2024', 'Title: "Elis SNC Quarterly Dividend"\nCollections: Companies\nDatasets: CompanyComputedRatiosV2\nChart Type: timeseries:eav_v2\nCanonical forms: "Dividend"="computed_ratio_dividend_yield"\nSources: S&P Global'],
+    ['How much did Meta pay in equity compensation?', 'Title: "Meta Quarterly Return on Equity"\nCollections: Companies\nDatasets: CompanyComputedRatiosV2\nChart Type: timeseries:eav_v2\nCanonical forms: "Return on Equity"="computed_ratio_return_on_equity"\nSources: S&P Global'],
+    ["Determine Tesla's quick ratio for the year 2023.", 'Title: "Quick Annual Ratio Quick"\nCollections: Companies\nDatasets: CompanyComputedRatiosV2\nChart Type: timeseries:eav_v2\nCanonical forms: "Ratio Quick"="computed_ratio_quick_ratio"\nSources: S&P Global'],
 ]
 scores = model.predict(pairs)
 print(scores.shape)
@@ -82,13 +82,13 @@ print(scores.shape)
 
 # Or rank different texts based on similarity to a single text
 ranks = model.rank(
-    'Cohere funding history: amounts raised by round',
+    'Which neighborhoods in Yonkers have the highest one-bedroom apartment rents?',
     [
-        'Title: "Cohere Overview"\nCollections: Companies\nChart Type: company:private\nSources: S&P Global',
-        'Title: "Turin F.C. Schedule"\nCollections: Soccer\nChart Type: schedule:soccer_team_v2',
-        'Title: "Tusculum, TN Inventory - House"\nCollections: Residential Real Estate\nDatasets: RegionalRealEstateIndicators\nChart Type: timeseries:eav_v2\nCanonical forms: "Inventory"="inventory_seasonally_unadjusted"\nSources: Redfin',
-        'Title: "Tesla Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Tesla"="Tesla, Inc.", "Overview"="Stock Overview"\nSources: S&P Global',
-        'Title: "Hong Kong Weather"\nCollections: Weather Forecasts\nChart Type: weather:international_forecast\nSources: OpenWeather',
+        'Title: "Yonkers, NY 2 Bedroom Apartment Rent"\nCollections: Residential Real Estate\nDatasets: RegionalApartmentRentIndicators\nChart Type: timeseries:eav_v2\nCanonical forms: "Rent"="average_rent_price"\nSources: ApartmentList',
+        'Title: "Multi-Chem Ltd. Overview"\nCollections: Companies\nChart Type: company:finance\nCanonical forms: "Multi-Chem Ltd."="Multi-Chem Limited", "Overview"="Stock Overview"\nSources: S&P Global',
+        'Title: "Elis SNC Quarterly Dividend"\nCollections: Companies\nDatasets: CompanyComputedRatiosV2\nChart Type: timeseries:eav_v2\nCanonical forms: "Dividend"="computed_ratio_dividend_yield"\nSources: S&P Global',
+        'Title: "Meta Quarterly Return on Equity"\nCollections: Companies\nDatasets: CompanyComputedRatiosV2\nChart Type: timeseries:eav_v2\nCanonical forms: "Return on Equity"="computed_ratio_return_on_equity"\nSources: S&P Global',
+        'Title: "Quick Annual Ratio Quick"\nCollections: Companies\nDatasets: CompanyComputedRatiosV2\nChart Type: timeseries:eav_v2\nCanonical forms: "Ratio Quick"="computed_ratio_quick_ratio"\nSources: S&P Global',
     ]
 )
 # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
@@ -129,8 +129,8 @@ You can finetune this model on your own dataset.
 
 | Metric       | Value      |
 |:-------------|:-----------|
-| pearson      | 0.8889     |
-| **spearman** | **0.8845** |
+| pearson      | 0.7897     |
+| **spearman** | **0.8409** |
 
 <!--
 ## Bias, Risks and Limitations
@@ -156,13 +156,13 @@ You can finetune this model on your own dataset.
   |         | sentence_0                                                                                     | sentence_1                                                                                       | label                                                          |
   |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
   | type    | string                                                                                         | string                                                                                           | float                                                          |
-  | details | <ul><li>min: 4 characters</li><li>mean: 44.22 characters</li><li>max: 116 characters</li></ul> | <ul><li>min: 75 characters</li><li>mean: 184.59 characters</li><li>max: 383 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
+  | details | <ul><li>min: 5 characters</li><li>mean: 43.38 characters</li><li>max: 116 characters</li></ul> | <ul><li>min: 75 characters</li><li>mean: 184.12 characters</li><li>max: 375 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.46</li><li>max: 1.0</li></ul> |
 * Samples:
-  | sentence_0                                                   | sentence_1                                                                                                                                                                                                                                                   | label             |
-  |:-------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
-  | <code>Cohere funding history: amounts raised by round</code> | <code>Title: "Cohere Overview"<br>Collections: Companies<br>Chart Type: company:private<br>Sources: S&P Global</code>                                                                                                                                        | <code>0.75</code> |
-  | <code>villes sympa à voir entre turin et come</code>         | <code>Title: "Turin F.C. Schedule"<br>Collections: Soccer<br>Chart Type: schedule:soccer_team_v2</code>                                                                                                                                                      | <code>0.0</code>  |
-  | <code>Current housing inventory in Chattanooga, TN</code>    | <code>Title: "Tusculum, TN Inventory - House"<br>Collections: Residential Real Estate<br>Datasets: RegionalRealEstateIndicators<br>Chart Type: timeseries:eav_v2<br>Canonical forms: "Inventory"="inventory_seasonally_unadjusted"<br>Sources: Redfin</code> | <code>0.25</code> |
+  | sentence_0                                                                                | sentence_1                                                                                                                                                                                                                                                 | label             |
+  |:------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
+  | <code>Which neighborhoods in Yonkers have the highest one-bedroom apartment rents?</code> | <code>Title: "Yonkers, NY 2 Bedroom Apartment Rent"<br>Collections: Residential Real Estate<br>Datasets: RegionalApartmentRentIndicators<br>Chart Type: timeseries:eav_v2<br>Canonical forms: "Rent"="average_rent_price"<br>Sources: ApartmentList</code> | <code>0.25</code> |
+  | <code>MLB team rankings</code>                                                            | <code>Title: "Multi-Chem Ltd. Overview"<br>Collections: Companies<br>Chart Type: company:finance<br>Canonical forms: "Multi-Chem Ltd."="Multi-Chem Limited", "Overview"="Stock Overview"<br>Sources: S&P Global</code>                                     | <code>0.0</code>  |
+  | <code>Eli Lilly dividend history 2014 to 2024</code>                                      | <code>Title: "Elis SNC Quarterly Dividend"<br>Collections: Companies<br>Datasets: CompanyComputedRatiosV2<br>Chart Type: timeseries:eav_v2<br>Canonical forms: "Dividend"="computed_ratio_dividend_yield"<br>Sources: S&P Global</code>                    | <code>0.0</code>  |
 * Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
   ```json
   {
@@ -307,22 +307,22 @@ You can finetune this model on your own dataset.
 ### Training Logs
 | Epoch  | Step | Training Loss | validation_spearman |
 |:------:|:----:|:-------------:|:-------------------:|
-| 0.4098 | 100  | -             | 0.8203              |
-| 0.8197 | 200  | -             | 0.8565              |
-| 1.0    | 244  | -             | 0.8587              |
-| 1.2295 | 300  | -             | 0.8632              |
-| 1.6393 | 400  | -             | 0.8772              |
-| 2.0    | 488  | -             | 0.8714              |
-| 2.0492 | 500  | 0.4207        | 0.8776              |
-| 2.4590 | 600  | -             | 0.8786              |
-| 2.8689 | 700  | -             | 0.8761              |
-| 3.0    | 732  | -             | 0.8824              |
-| 3.2787 | 800  | -             | 0.8817              |
-| 3.6885 | 900  | -             | 0.8838              |
-| 4.0    | 976  | -             | 0.8835              |
-| 4.0984 | 1000 | 0.3261        | 0.8836              |
-| 4.5082 | 1100 | -             | 0.8843              |
-| 4.9180 | 1200 | -             | 0.8845              |
+| 0.4098 | 100  | -             | 0.7046              |
+| 0.8197 | 200  | -             | 0.7775              |
+| 1.0    | 244  | -             | 0.7908              |
+| 1.2295 | 300  | -             | 0.8018              |
+| 1.6393 | 400  | -             | 0.8204              |
+| 2.0    | 488  | -             | 0.8211              |
+| 2.0492 | 500  | 0.541         | 0.8223              |
+| 2.4590 | 600  | -             | 0.8263              |
+| 2.8689 | 700  | -             | 0.8314              |
+| 3.0    | 732  | -             | 0.8347              |
+| 3.2787 | 800  | -             | 0.8379              |
+| 3.6885 | 900  | -             | 0.8390              |
+| 4.0    | 976  | -             | 0.8390              |
+| 4.0984 | 1000 | 0.4095        | 0.8385              |
+| 4.5082 | 1100 | -             | 0.8409              |
+| 4.9180 | 1200 | -             | 0.8409              |
 
 
 ### Framework Versions

config.json

@@ -1,55 +1,36 @@
 {
   "architectures": [
-    "ModernBertForSequenceClassification"
+    "XLMRobertaForSequenceClassification"
   ],
-  "attention_bias": false,
-  "attention_dropout": 0.0,
-  "bos_token_id": null,
-  "classifier_activation": "gelu",
-  "classifier_bias": false,
-  "classifier_dropout": 0.0,
-  "classifier_pooling": "mean",
-  "cls_token_id": 50281,
-  "decoder_bias": true,
-  "deterministic_flash_attn": false,
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
   "dtype": "float32",
-  "embedding_dropout": 0.0,
-  "eos_token_id": null,
-  "global_attn_every_n_layers": 3,
-  "global_rope_theta": 160000.0,
-  "gradient_checkpointing": false,
-  "hidden_activation": "gelu",
-  "hidden_size": 768,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
   "id2label": {
     "0": "LABEL_0"
   },
-  "initializer_cutoff_factor": 2.0,
   "initializer_range": 0.02,
-  "intermediate_size": 1152,
+  "intermediate_size": 1536,
   "label2id": {
     "LABEL_0": 0
   },
   "layer_norm_eps": 1e-05,
-  "local_attention": 128,
-  "local_rope_theta": 10000.0,
-  "max_position_embeddings": 8192,
-  "mlp_bias": false,
-  "mlp_dropout": 0.0,
-  "model_type": "modernbert",
-  "norm_bias": false,
-  "norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
   "num_attention_heads": 12,
-  "num_hidden_layers": 22,
-  "pad_token_id": 50283,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
   "position_embedding_type": "absolute",
-  "repad_logits_with_grad": false,
   "sentence_transformers": {
-    "activation_fn": "torch.nn.modules.activation.Sigmoid",
+    "activation_fn": "torch.nn.modules.linear.Identity",
     "version": "5.1.1"
   },
-  "sep_token_id": 50282,
-  "sparse_pred_ignore_index": -100,
-  "sparse_prediction": false,
   "transformers_version": "4.57.1",
-  "vocab_size": 50368
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
 }

eval/CrossEncoderCorrelationEvaluator_validation_results.csv

@@ -1,6 +1,6 @@
 epoch,steps,Pearson_Correlation,Spearman_Correlation
-1.0,244,0.8620642924096914,0.8587166361363444
-2.0,488,0.8764832585164201,0.8713859435370955
-3.0,732,0.8867003524365638,0.8823857804088827
-4.0,976,0.8881431986959347,0.8835376105032559
-5.0,1220,0.8889602207955667,0.8845866499868097
+1.0,244,0.7586113740254306,0.7907668333684233
+2.0,488,0.7744378519117527,0.8210875876975176
+3.0,732,0.7822930336104869,0.8346728878134918
+4.0,976,0.7832125998627666,0.8389889573353402
+5.0,1220,0.7895515616112064,0.8408202633317214

model.safetensors

@@ -1,3 +1,3 @@
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-size 598436708
+oid sha256:7025f18b2a2b9819f22d09477f431c143b6251c716df8f3267920ff1b161a94d
+size 470588492

special_tokens_map.json

@@ -1,34 +1,48 @@
 {
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
   "cls_token": {
-    "content": "[CLS]",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
     "lstrip": false,
     "normalized": false,
     "rstrip": false,
     "single_word": false
   },
   "mask_token": {
-    "content": "[MASK]",
+    "content": "<mask>",
     "lstrip": true,
     "normalized": false,
     "rstrip": false,
     "single_word": false
   },
   "pad_token": {
-    "content": "[PAD]",
+    "content": "<pad>",
     "lstrip": false,
     "normalized": false,
     "rstrip": false,
     "single_word": false
   },
   "sep_token": {
-    "content": "[SEP]",
+    "content": "</s>",
     "lstrip": false,
     "normalized": false,
     "rstrip": false,
     "single_word": false
   },
   "unk_token": {
-    "content": "[UNK]",
+    "content": "<unk>",
     "lstrip": false,
     "normalized": false,
     "rstrip": false,

tokenizer.json

@@ -1,3 +1,3 @@
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+oid sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
+size 17082987

tokenizer_config.json

@@ -1,952 +1,55 @@
 {
   "added_tokens_decoder": {
     "0": {
-      "content": "|||IP_ADDRESS|||",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
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-    "1": {
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       "normalized": false,
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       "single_word": false,
       "special": true
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+  "bos_token": "<s>",
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+  "cls_token": "<s>",
+  "eos_token": "</s>",
   "extra_special_tokens": {},
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+  "mask_token": "<mask>",
   "model_max_length": 512,
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-  "tokenizer_class": "PreTrainedTokenizerFast",
-  "truncation_side": "right",
-  "truncation_strategy": "longest_first",
-  "unk_token": "[UNK]"
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
 }

training_info.txt

@@ -1,4 +1,4 @@
-Base Model: Alibaba-NLP/gte-reranker-modernbert-base
+Base Model: cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
 Training Samples: 7779
 Epochs: 5
 Batch Size: 32