Revert inadvertent config, tokenizer updates

This reverts commit 15192f12d2403ed1fd7c16a8841379096c5d4a9b.

README.md

@@ -1,75 +1,75 @@
----
-license: apache-2.0
-datasets:
-- sentence-transformers/msmarco
-language:
-- en
-base_model:
-- nreimers/BERT-Tiny_L-2_H-128_A-2
-pipeline_tag: text-ranking
-library_name: sentence-transformers
-tags:
-- transformers
----
-# Cross-Encoder for MS Marco
-
-This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
-
-The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
-
-
-## Usage with Transformers
-
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
-model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-TinyBERT-L2-v2')
-tokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-TinyBERT-L2-v2')
-
-features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'],  padding=True, truncation=True, return_tensors="pt")
-
-model.eval()
-with torch.no_grad():
-    scores = model(**features).logits
-    print(scores)
-```
-
-
-## Usage with SentenceTransformers
-
-The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
-```python
-from sentence_transformers import CrossEncoder
-
-model = CrossEncoder('cross-encoder/ms-marco-TinyBERT-L2-v2', max_length=512)
-scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
-```
-
-
-## Performance
-In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset. 
-
-
-| Model-Name        | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev)  | Docs / Sec |
-| ------------- |:-------------| -----| --- | 
-| **Version 2 models** | | | 
-| cross-encoder/ms-marco-TinyBERT-L2-v2 | 69.84 | 32.56 | 9000
-| cross-encoder/ms-marco-MiniLM-L2-v2 | 71.01 | 34.85 | 4100
-| cross-encoder/ms-marco-MiniLM-L4-v2 | 73.04 | 37.70 | 2500
-| cross-encoder/ms-marco-MiniLM-L6-v2 | 74.30 | 39.01 | 1800
-| cross-encoder/ms-marco-MiniLM-L12-v2 | 74.31 | 39.02 | 960
-| **Version 1 models** | | | 
-| cross-encoder/ms-marco-TinyBERT-L2  | 67.43 | 30.15  | 9000
-| cross-encoder/ms-marco-TinyBERT-L4  | 68.09 | 34.50  | 2900
-| cross-encoder/ms-marco-TinyBERT-L6 |  69.57 | 36.13  | 680
-| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
-| **Other models** | | | 
-| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900 
-| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340 
-| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100 
-| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340 
-| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330 
-| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
- 
- Note: Runtime was computed on a V100 GPU.
+---
+license: apache-2.0
+datasets:
+- sentence-transformers/msmarco
+language:
+- en
+base_model:
+- nreimers/BERT-Tiny_L-2_H-128_A-2
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+tags:
+- transformers
+---
+# Cross-Encoder for MS Marco
+
+This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
+
+The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
+
+
+## Usage with Transformers
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-TinyBERT-L2-v2')
+tokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-TinyBERT-L2-v2')
+
+features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'],  padding=True, truncation=True, return_tensors="pt")
+
+model.eval()
+with torch.no_grad():
+    scores = model(**features).logits
+    print(scores)
+```
+
+
+## Usage with SentenceTransformers
+
+The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
+```python
+from sentence_transformers import CrossEncoder
+
+model = CrossEncoder('cross-encoder/ms-marco-TinyBERT-L2-v2', max_length=512)
+scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
+```
+
+
+## Performance
+In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset. 
+
+
+| Model-Name        | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev)  | Docs / Sec |
+| ------------- |:-------------| -----| --- | 
+| **Version 2 models** | | | 
+| cross-encoder/ms-marco-TinyBERT-L2-v2 | 69.84 | 32.56 | 9000
+| cross-encoder/ms-marco-MiniLM-L2-v2 | 71.01 | 34.85 | 4100
+| cross-encoder/ms-marco-MiniLM-L4-v2 | 73.04 | 37.70 | 2500
+| cross-encoder/ms-marco-MiniLM-L6-v2 | 74.30 | 39.01 | 1800
+| cross-encoder/ms-marco-MiniLM-L12-v2 | 74.31 | 39.02 | 960
+| **Version 1 models** | | | 
+| cross-encoder/ms-marco-TinyBERT-L2  | 67.43 | 30.15  | 9000
+| cross-encoder/ms-marco-TinyBERT-L4  | 68.09 | 34.50  | 2900
+| cross-encoder/ms-marco-TinyBERT-L6 |  69.57 | 36.13  | 680
+| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
+| **Other models** | | | 
+| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900 
+| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340 
+| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100 
+| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340 
+| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330 
+| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
+ 
+ Note: Runtime was computed on a V100 GPU.

config.json

@@ -1,34 +1,31 @@
-{
-  "architectures": [
-    "BertForSequenceClassification"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 128,
-  "id2label": {
-    "0": "LABEL_0"
-  },
-  "initializer_range": 0.02,
-  "intermediate_size": 512,
-  "label2id": {
-    "LABEL_0": 0
-  },
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 2,
-  "num_hidden_layers": 2,
-  "pad_token_id": 0,
-  "position_embedding_type": "absolute",
-  "sentence_transformers": {
-    "activation_fn": "torch.nn.modules.linear.Identity",
-    "version": "4.1.0.dev0"
-  },
-  "transformers_version": "4.52.0.dev0",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 30522
-}
+{
+  "_name_or_path": "nreimers/BERT-Tiny_L-2_H-128_A-2",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 128,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 512,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 2,
+  "num_hidden_layers": 2,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.4.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522,
+  "sbert_ce_default_activation_function": "torch.nn.modules.linear.Identity"
+}

special_tokens_map.json

@@ -1,37 +1,7 @@
 {
-  "cls_token": {
-    "content": "[CLS]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "mask_token": {
-    "content": "[MASK]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": {
-    "content": "[PAD]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "sep_token": {
-    "content": "[SEP]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "unk_token": {
-    "content": "[UNK]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  }
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
 }